geog 8450 geospatial tools in 8/12/2008 landscape...

GEOG 8450 Geospatial Tools in Landscape Analysis

8/12/2008

Madden Fall 2006 1

Kim, Xu and Madden UGA-CRMS GEOBIA 2008, Univ. of Calgary

•Object-based Vegetation Type Mapping using

•Ikonos Imagery: Spectral, Spatial and

Topographic Information

www.crms.uga.eduCRMS

Minho KimBo Xu

Marguerite Madden

Center for Remote Sensing and Mapping Science (CRMS)Department of GeographyUniversity of Georgia, USA

. Kim, Xu and Madden UGA-CRMS GEOBIA 2008, Univ. of Calgary

Presentation Objectives:

Background on UGA CRMS-NPS vegetation database development of Great Smoky Mountains National Park by manual interpretation.

Assess the use of geospatial object-based image analysis (GEOBIA) to capture expert knowledge and semi/automate forest cover mapping.

Present results from Kim and Xu on classification accuracies with spectral, texture, topographic and contextual information – and vegetation aggregation.





Present results from Kim and Xu on classification accuracies with spectral, texture, topographic and contextual information – and vegetation aggregation.





Present results from Minho Kim and Bo Xu on classification accuracies with spectral, texture, topographic and contextual information – and vegetation aggregation.

Kim, Xu and Madden UGA-CRMS GEOBIA 2008, Univ. of Calgary Kim, Xu and Madden UGA-CRMS GEOBIA 2008, Univ. of Calgary

Digital Vegetation Databases/Maps for National Park Units of the Southeast

Everglades National ParkBig Cypress National PreserveBiscayne National ParkGreat Smoky Mountains National ParkMammoth Cave National Park Little River Natl. Canyon National PreserveBig South Fork Natl. River & Recreation AreaCumberland Gap National Historical Park Blue Ridge ParkwayObed Wild & Scenic RiverGuilford Courthouse Natl. Military ParkNinety Six National Historic Park

USGS/NPS National Vegetation Mapping Program

Carl Sandburg Home National Historic Site Abraham Lincoln National Historic SiteFort Donaldson National BattlefieldStones River National Battlefield Cowpens National BattlefieldRussell Cave National MonumentKings Mountain National Military ParkShiloh National Military ParkChickamauga National Military ParkChattanooga National Military Park


8/12/2008

Madden Fall 2006 2


Mapping and GIS Analysis of Vegetation in Great Smoky Mountains National Park, NPS

Cherokee

Gatlinburg

2025 m

10 km

250 m

Elevation range from 250 to 2025 m AMSLNearly continuous forest cover= Photogrammetric challenge Kim, Xu and Madden UGA-CRMS GEOBIA 2008, Univ. of Calgary

Manual interpretation of forest communities from color infrared (CIR) photos acquired in the fall


1400 Photos at 1:12,000 Scale


Spatial Accuracy ~ +/- 5 – 10 m RMSE


Wear Cove Ortho-corrected Raw Vectors

Wear Cove Edited Vectors

Wear Cove Final Vegetation Map


Fieldwork is conducted by CRMS, NPS and NatureServe to verify interpretation, establish vegetation classes and refine the rule sets for fire fuel classes.

Kodak Digital Field Imaging System (FIS) =

DC265 Digital Camera +Garmin III Plus GPS


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Madden Fall 2006 3


Great Smoky Mountains National ParkVegetation Classification System

I. FORESTA. Sub Alpine Forest

1. Fraser Fir Fa. Formerly Fraser Fir (F)

2. Red Spruce – Fraser Fir S-F, S/F, F/Sa. Red Spruce – Fraser Fir/Rhododendron S-F/Rb. Red Spruce – Fraser Fir/Low Shrub-Herb S-F/Sh

3. Red Spruce Sa. Red Spruce/Rhododendron S/Rb. Red Spruce/Birch S/NHx:Bc. Red Spruce/Hemlock S/T, T/Sd. Red Spruce and/or Hemlock (uncertain) S.T

4. Exposed Northern Hardwoods NhxEa. Exposed Northern Hardwoods/Red Spruce NHxE/S

Special Modifiers

Damage (cause unknown, by landslide, by insects, by wind) -1, -2, -3, -4 Post disturbance recovery -5Human influence -6Abandoned agriculture -7Grape vines -8Recently logged -9Recently burned -10


Manual photo interpretation discriminated over 100 vegetation classes.

Thematic accuracy by NPS (Jenkins 2007)Overall classification averaged 81%


3-Tiered Attribution SystemDominant VegetationSecond VegetationThird Vegetation


Analysis and visualization of vegetationdistributions with respect to environmental factors


Thunderhead Mountain

Overstory Vegetation

Thunderhead Mountain

Digital Elevation Model


Elevation

Slope

Aspect


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Madden Fall 2006 4

Kim, Xu and Madden UGA-CRMS GEOBIA 2008, Univ. of Calgary0

100

200

300

400

500

600

700

800

900

1000

N NE E SE SE SW W NW

Aspect

Are

a (Ha)

Cove Hardwoods (CHx)

+

Thunderhead Mountain Vegetation and Elevation Range

0

200

400

600

800

1000

1200

1400

1600

1800

PIRD W

OzH

OzHf

HxCHx

Om

H TO

cHM

AL HINHx

MO

MO/H

th KR-K R P

General Vegetation Classes

Ele

vati

on

(m

)

Min

MaxMean

+

Thunderhead Mountain Vegetation and Slope

0

10

20

30

40

50

60

PIRD W

OzHOzH

fHx

CHxOm

H TOcH

MAL HINHx

MO

MO/Hth K

R-K R P

General Vegetation Classes

Slo

pe

(deg

rees

)

MinMaxMean

55

+

P(CHx) = .29 (North aspects 0 to 23o or 338 to 360o) + .19 (Northeast aspects 23 to 68o) + .31 (Northwest aspects 293 to 338o).

Empirically-Based Rule Set

Elevation Range

Slope

Aspect


Spatial correlations used to define rule sets for vegetation types

F

MO-Hth MO CHx

Montane Oak-Heath (MO-Hth)

Montane Oak(MO)

Cove Hardwood (CHx)


Manually interpreted vegetation database as reference data set for accuracy assessment


Graminoid PasturePPPGrass

Heath Bald/Northern HardwoodsHeath Bald /Shrub (Sb)

Hth/NHxHth/Sb

HthHthShrub

Cove Hardwoods (CHx)Rich Type (CHxR) with Hemlock (T)

CHxRCHxR/T

CHxRCHxMixed

Northern HardwoodsYellow Birch Type (NHxB)Beech Gap (NHxBe)Acid Type (NHxA)Rich Type (NHxR)Montane Northern Red Oak (MOr) withRhododendron-Kalmia (R-K), Heath Bald (Hth) or graminoidMixed Hardwoods Acid Type (HxA) with Eastern Hemlock (T)

NHxBeNHxA

NHxRMOr/R-KMOr/Hth

MOr/GHxAHxA/T

NHxNHxA

NHxRMOrHx

NHxMOr

Hx

Deciduous

Eastern Hemlock (T) and Mixed Northern Hardwood-Acid Type (NHxA)or Successional Mixed, Acidic (HxA)

T/NHxAT/HxA

TTConiferous

Association Description15-Class9-Class7-Class5-Class

Aggregated Forest Type/Community Classification Schema


Segmentation: Scale Parameter 250


Defined Training Samples (7-Class) Forest Types


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Madden Fall 2006 5


Classification Accuracy: Aggregated Forest Classe and Scale Parameter

0. 2

0. 3

0. 4

0. 5

0. 6

0. 7

0. 8

0. 9

1

50 100 150 200 250 300

Scale parameter

Acc

urac

y

5 classes

7 classes

9 classes

15 classes

Cla

ssifi

catio

n A

ccur

acy

Scale Parameter

5 Classes7 Classes9 Classes15 Classes


Classification accuracy: (7-class schema)Spectral, Texture and Topographic Information

0.370.70Spectral mean

0.390.70Spectral mean + Contrast + Correlation+ Entropy

0.390.69Spectral mean + Contrast

0.380.69Spectral mean + Correlation

0.390.69Spectral mean + Contrast + Entropy

0.380.69Spectral mean + Contrast + Correlation

0.350.67Spectral mean + Spectral Standard Deviation

KappaAccuracyFuzzy rules


Classification accuracy: (7-class schema)Spectral, Texture and Topographic Information

0.420.73Spectral mean + DEM + Entropy

0.400.73Spectral mean + DEM

0.220.71Spectral mean + DEM + Slope + Aspect

0.220.71Spectral mean + DEM + Slope

0.390.71Spectral mean + DEM + Aspect

0.390.71Spectral mean + Entropy

0.210.71Spectral mean + Aspect + Slope

0.210.70Spectral mean + Slope

0.370.70Spectral mean + Aspect

0.390.70Spectral mean + Entropy + Correlation

0.370.70Spectral mean

KappaAccuracyFuzzy rules


Classification Accuracy :

Forest Aggregation, Topography and Texture

Highest classification accuracy for each vegetation class schema

0.460.90Spectral + DEM +

Aspect5-class

0.420.73Spectral + DEM

+ Entropy7-class

0.360.49Spectral + DEM+ Slope9-class

0.320.45Spectral + DEM15-class

KappaOverall

accuracyFuzzy rulesClass schema


5 Class

Manual Interpretation Definiens Professional 5.0

7 Class


Manual Interpretation Definiens Professional 5.0

9 Class

15 Class


8/12/2008

Madden Fall 2006 6


Classification Accuracy:Spectral Mean, Topography and Entropy

as Decision Rules (7-class schema)

0.42Kappa index

0.73Overall accuracy

0.390.420.850.540.290.490.40User's accuracy

0.520.130.810.590.390.400.31Producer's accuracy

TPNHxMOrHxHthCHxClasses


Fuzzy Class Membership: Three-tiered Classes

MOr: 0.95Hth: 0.98NHx:0.99HthNHx5

T:0.90NHx: 0.99Hx: 0.99THx4

Hx: 0.91NHx: 0.98MOr: 1.00HthMOr3

Hth: 0.87MOr: 0.98NHx: 0.99NHxMOr2

Hth: 0.93NHx 0.99HthNHx1

ThirdSecondHighestSecondDominant

Classified Map (Membership)Reference map

Object


GEOBIA Ikonos Smokemont Study Area:Great Smoky Mountains National Park


Methodology(a) (b)

Ikonos ImageOctober 30, 2003

CIR Orthophoto ReferenceOctober 27, 1997 (1:12,000)

Definiens Developer 7.0


(a) (b)

Vegetation Types: Manual DEM (10-m)

DeciduousEvergreenShrubOther

Elevation (m)High 1579

Low 609


Classification accuracies

30

35

40

45

50

55

60

65

70

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Segmentation scale

Percentage

overall kappa*100

Classification Accuracy: Spectral Mean

67.07/0.44

Cla

ssifi

catio

n A

ccur

acy

%

Segmentation Scale


8/12/2008

Madden Fall 2006 7


Classification: Spectral Mean (Scale Parameter 65)

Segments over Vegetation Manual Classified Segments


Classification accuracies

25

30

35

40

45

50

55

60

65

70

75

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Segmentation scale

Percentage

overall kappa*100

71.32/0.48

Classification Accuracy: Spectral Mean and Topographic Information

Segmentation Scale

Cla

ssifi

catio

n A

ccur

acy

%


Classification: Spectral Mean and Topographic Information (Scale Parameter 75)



Streams over manual interpretation of forest types

Addition of Stream Channels to Improve Segmentation of Valley Hemlock Communities

Rasterized buffer distances from stream channels.


Scale 48: 76.6/0.57

Classification Accuracy: Spectral Mean, Topography and Stream Channels

Cla

ssifi

catio

n A

ccur

acy

%

Segmentation Scale


Classification Accuracy: Spectral Mean, Topography and Stream Channels (Scale Parameter 48)



8/12/2008

Madden Fall 2006 8


Comparison of Classification Performance

+ 5.6- 2.2+ 4.9- 10.0Grass

- 3.2- 6.6- 8.3- 3.9Shrub

+14.2+5.6+1.5- 7.0Mixed

+15.5-1.7+7.7-3.4Evergreen

+2.1+14.3+0.5+10.0Deciduous

User’s Accuracy %

Producer’s Accuracy %

User’s Accuracy %

Producer’s Accuracy %

VegetationType

Spectral Mean - Topography Spectral Mean – Topo/Streams


Deciduous ForestMaximum gain of 14.3 % in producer’s accuracy by adding

topographic variables and buffer distance of stream channels

Evergreen forestIncrease of producer’s accuracy with slight decrease of

user’s accuracy by adding topography and stream buffers. Maximum gain of 15.5 % in user’s accuracy

Mixed forestIncreased individual accuracies by adding topographic

variables and buffer distance of stream channelsMaximum gain of 14.2 % in user’s accuracy

Summary


• Shrub- Decrease of accuracies when adding ancillary

information. Maximum loss of 8.3 % in user’s accuracy by adding topographic variables

• Grass- Decrease in producer’s accuracy after adding ancillary

information. Maximum loss of 10 %

- Increase in user’s accuracy with the addition of ancillary information. Maximum gain of 5.6 %

Summary


Manual Spectral

SpectralTopo

SpectralTopoStreams


Spectral information alone did not produce appropriate segmentation results particularly for continuous features such as forest types and narrow shaped vegetation communities.

Texture, topography and context (i.e., proximity to stream channels) improved segmentation quality and classification, especially for forest types in mountainous areas.

Future research will continue to explore GEOBIA methods for expert knowledge preservation and semi-automation of vegetation mapping.

Conclusions


Thank you

Center for Remote Sensing and Mapping ScienceDepartment of Geography, The University of Georgiahttp://www.crms.uga.edu

CRMS

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