Texture Metric Comparison of Manual Forest Stand Delineation and Image Segmentation Richard M. Warnick, Ken Brewer, Kevin Megown, Mark Finco, and Brian

Texture Metric Comparison of Texture Metric Comparison of Manual Forest Stand Delineation Manual Forest Stand Delineation

and Image Segmentationand Image Segmentation

Texture Metric Comparison of Texture Metric Comparison of Manual Forest Stand Delineation Manual Forest Stand Delineation

and Image Segmentationand Image Segmentation

Richard M. Warnick, Ken Brewer, Richard M. Warnick, Ken Brewer,

Kevin Megown, Mark Finco, Kevin Megown, Mark Finco,

and Brian Schwindand Brian SchwindUSDA Forest Service Remote Sensing Applications CenterUSDA Forest Service Remote Sensing Applications Center

Ralph WarbingtonRalph WarbingtonUSDA Forest Service Pacific Southwest RegionUSDA Forest Service Pacific Southwest Region

Jim BarberJim BarberUSDA Forest Service Northern RegionUSDA Forest Service Northern Region

RS 2006 • April 26, 2006RS 2006 • April 26, 2006

Richard M. Warnick, Ken Brewer, Richard M. Warnick, Ken Brewer,

Kevin Megown, Mark Finco, Kevin Megown, Mark Finco,

and Brian Schwindand Brian SchwindUSDA Forest Service Remote Sensing Applications CenterUSDA Forest Service Remote Sensing Applications Center

Ralph WarbingtonRalph WarbingtonUSDA Forest Service Pacific Southwest RegionUSDA Forest Service Pacific Southwest Region

Jim BarberJim BarberUSDA Forest Service Northern RegionUSDA Forest Service Northern Region

RS 2006 • April 26, 2006RS 2006 • April 26, 2006

USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us

IntroductionIntroductionIntroductionIntroduction

Cornerstone assumption: Cornerstone assumption: Stand-Stand-level forest models work best with homogeneous level forest models work best with homogeneous unitsunits

Is there an overall difference in textural homogeneity Is there an overall difference in textural homogeneity between manual stand delineation and image between manual stand delineation and image segmentation polygons for the same study area?segmentation polygons for the same study area?

Cornerstone assumption: Cornerstone assumption: Stand-Stand-level forest models work best with homogeneous level forest models work best with homogeneous unitsunits

Is there an overall difference in textural homogeneity Is there an overall difference in textural homogeneity between manual stand delineation and image between manual stand delineation and image segmentation polygons for the same study area?segmentation polygons for the same study area?

STAND BOUNDARIESSTAND BOUNDARIES IMAGE SEGMENTATIONIMAGE SEGMENTATION


IntroductionIntroductionIntroductionIntroduction

Utilize texture metricsUtilize texture metrics Gray-Level Co-occurrence Matrix (GLCM) metricsGray-Level Co-occurrence Matrix (GLCM) metrics

CorrelationCorrelation EntropyEntropy MeanMean VarianceVariance

Utilize texture metricsUtilize texture metrics Gray-Level Co-occurrence Matrix (GLCM) metricsGray-Level Co-occurrence Matrix (GLCM) metrics

CorrelationCorrelation EntropyEntropy MeanMean VarianceVariance

Compare manual interpretation and image Compare manual interpretation and image segmentation for forest stand delineationsegmentation for forest stand delineation Vertical stereo photo interpretationVertical stereo photo interpretation eCognition™ image segmentation from Landsat ETM+eCognition™ image segmentation from Landsat ETM+

Compare manual interpretation and image Compare manual interpretation and image segmentation for forest stand delineationsegmentation for forest stand delineation Vertical stereo photo interpretationVertical stereo photo interpretation eCognition™ image segmentation from Landsat ETM+eCognition™ image segmentation from Landsat ETM+


Study areaStudy areaStudy areaStudy area

Idaho Panhandle National Forests, Idaho/Montana/WashingtonIdaho Panhandle National Forests, Idaho/Montana/Washington

Study area covers most of Kaniksu NFStudy area covers most of Kaniksu NF

Idaho Panhandle National Forests, Idaho/Montana/WashingtonIdaho Panhandle National Forests, Idaho/Montana/Washington

Study area covers most of Kaniksu NFStudy area covers most of Kaniksu NF

IDAHO


Study areaStudy areaStudy areaStudy area

Study area Landsat view, Idaho/MontanaStudy area Landsat view, Idaho/MontanaStudy area Landsat view, Idaho/MontanaStudy area Landsat view, Idaho/Montana


GIS datasetsGIS datasetsGIS datasetsGIS datasets

Idaho Panhandle National Idaho Panhandle National Forests stand boundariesForests stand boundaries

Vertical stereo photography Vertical stereo photography (1985 - 1998)(1985 - 1998)

Manual stand delineation Manual stand delineation (1980s – 1990s)(1980s – 1990s)

Northern Region Vegetation Northern Region Vegetation Mapping Project Mapping Project

Landsat ETM+ July/August Landsat ETM+ July/August 20022002

Image segmentation using Image segmentation using eCognition™eCognition™

Idaho Panhandle National Idaho Panhandle National Forests stand boundariesForests stand boundaries

Vertical stereo photography Vertical stereo photography (1985 - 1998)(1985 - 1998)

Manual stand delineation Manual stand delineation (1980s – 1990s)(1980s – 1990s)

Northern Region Vegetation Northern Region Vegetation Mapping Project Mapping Project

Landsat ETM+ July/August Landsat ETM+ July/August 20022002

Image segmentation using Image segmentation using eCognition™eCognition™


ImageryImageryImageryImagery

Landsat ETM+ panchromatic band (15 m)Landsat ETM+ panchromatic band (15 m) 2002 image subset to project area2002 image subset to project area

IRS 1-C panchromatic (5 m)IRS 1-C panchromatic (5 m) 1998 image subset to project area1998 image subset to project area

DOQQ mosaic (1 m)DOQQ mosaic (1 m) 90 digital ortho quarter quads (1980s-1990s)90 digital ortho quarter quads (1980s-1990s)

NAIP mosaic principal component (1 m)NAIP mosaic principal component (1 m) 2004 National Agricultural Imagery Program color 2004 National Agricultural Imagery Program color

county mosaic subset to project areacounty mosaic subset to project area First principal component image generated to reduce First principal component image generated to reduce

image to one bandimage to one band

Landsat ETM+ panchromatic band (15 m)Landsat ETM+ panchromatic band (15 m) 2002 image subset to project area2002 image subset to project area

IRS 1-C panchromatic (5 m)IRS 1-C panchromatic (5 m) 1998 image subset to project area1998 image subset to project area

DOQQ mosaic (1 m)DOQQ mosaic (1 m) 90 digital ortho quarter quads (1980s-1990s)90 digital ortho quarter quads (1980s-1990s)

NAIP mosaic principal component (1 m)NAIP mosaic principal component (1 m) 2004 National Agricultural Imagery Program color 2004 National Agricultural Imagery Program color

county mosaic subset to project areacounty mosaic subset to project area First principal component image generated to reduce First principal component image generated to reduce

image to one bandimage to one band


Texture metricsTexture metricsTexture metricsTexture metrics

Selection of texture measuresSelection of texture measures

Relatively simple and repeatableRelatively simple and repeatable Can be run on commercial software (ENVICan be run on commercial software (ENVI®®)) Adaptable to corporate software (ERDAS Imagine)Adaptable to corporate software (ERDAS Imagine) Technique common in remote sensing literatureTechnique common in remote sensing literature Need four texture measures, not highly correlated Need four texture measures, not highly correlated

Relatively simple and repeatableRelatively simple and repeatable Can be run on commercial software (ENVICan be run on commercial software (ENVI®®)) Adaptable to corporate software (ERDAS Imagine)Adaptable to corporate software (ERDAS Imagine) Technique common in remote sensing literatureTechnique common in remote sensing literature Need four texture measures, not highly correlated Need four texture measures, not highly correlated

Gray-level Co-occurrence Matrix (GLCM)

GLCM texture tutorial by Mryka Hall-Beyer

http://www.fp.ucalgary.ca/mhallbey/tutorial.htm

Gray-level Co-occurrence Matrix (GLCM)

GLCM texture tutorial by Mryka Hall-Beyer

http://www.fp.ucalgary.ca/mhallbey/tutorial.htm



GLCM CorrelationGLCM Correlation Measures the linear dependency of gray levels on those Measures the linear dependency of gray levels on those

of neighboring pixels in the GLCMof neighboring pixels in the GLCM GLCM EntropyGLCM Entropy

Measures the level of spatial disorder of gray levels in Measures the level of spatial disorder of gray levels in the GLCMthe GLCM

GLCM MeanGLCM Mean Measures the mean of theMeasures the mean of the probability values from the probability values from the

GLCMGLCM GLCM VarianceGLCM Variance

Measures the dispersion around the mean of Measures the dispersion around the mean of combinations of reference and neighbor pixels in the combinations of reference and neighbor pixels in the GLCMGLCM

GLCM CorrelationGLCM Correlation Measures the linear dependency of gray levels on those Measures the linear dependency of gray levels on those

of neighboring pixels in the GLCMof neighboring pixels in the GLCM GLCM EntropyGLCM Entropy

Measures the level of spatial disorder of gray levels in Measures the level of spatial disorder of gray levels in the GLCMthe GLCM

GLCM MeanGLCM Mean Measures the mean of theMeasures the mean of the probability values from the probability values from the

GLCMGLCM GLCM VarianceGLCM Variance

Measures the dispersion around the mean of Measures the dispersion around the mean of combinations of reference and neighbor pixels in the combinations of reference and neighbor pixels in the GLCMGLCM

Gray-Level Co-occurrence Matrix texture metrics



3 x 3 moving window



Image pixel DN

Sample window



3535 4545 5151 5454 5555 5959 6161

3535 00 00 00 00 00 00 00

4545 11 00 00 00 00 00 00

5151 00 00 00 00 00 00 00

5454 00 11 00 00 11 00 00

5555 00 00 00 11 00 00 00

5959 00 00 11 00 00 00 00

6161 00 00 00 00 00 11 00

Neighbor pixel DN

Refe

rence

pix

el D

N

GLCM for sample window

Distance from the diagonal is proportional to the amount of contrast


Image processingImage processingImage processingImage processing

ENVIENVI®® 4.2 used to generate 4.2 used to generate GLCM texture imagesGLCM texture images

GLCM correlationGLCM correlation GLCM entropyGLCM entropy GLCM meanGLCM mean GLCM varianceGLCM variance

ENVIENVI®® 4.2 used to generate 4.2 used to generate GLCM texture imagesGLCM texture images

GLCM correlationGLCM correlation GLCM entropyGLCM entropy GLCM meanGLCM mean GLCM varianceGLCM variance

CORRELATION ENTROPY

MEAN

VARIANCE


Image processingImage processingImage processingImage processing

M – mosaic S – subset PC – principal component T – GLCM texture


Zonal statisticsZonal statisticsZonal statisticsZonal statistics





6.41

8.00

5.115.77

5.29



6.09

8.77

5.25

5.005.18

8.967.51

5.65

7.51



Distribution of zonal means GLCM variance with NAIP

Distribution of zonal means GLCM variance with NAIP

STAND POLYGONS SEGMENTATION POLYGONSSTAND POLYGONS SEGMENTATION POLYGONS

Mean = 15.05

Std Dev = 9.945

N = 8,159

Mean = 14.98

Std Dev = 9.769

N = 33,468


ConclusionsConclusionsConclusionsConclusions

HypothesesHypotheses Segmentation polygons would be more homogeneous in Segmentation polygons would be more homogeneous in

texturetexture Represent existing vegetation instead of management unitsRepresent existing vegetation instead of management units

Polygons with smaller area are more homogeneousPolygons with smaller area are more homogeneous Texture differences may be minimizedTexture differences may be minimized

HypothesesHypotheses Segmentation polygons would be more homogeneous in Segmentation polygons would be more homogeneous in

texturetexture Represent existing vegetation instead of management unitsRepresent existing vegetation instead of management units

Polygons with smaller area are more homogeneousPolygons with smaller area are more homogeneous Texture differences may be minimizedTexture differences may be minimized

Preliminary findingsPreliminary findings Stand and segmentation polygons seem to be almost Stand and segmentation polygons seem to be almost

equally homogeneous, with a slight edge to the latterequally homogeneous, with a slight edge to the latter Average size of the polygons not reflected in differences Average size of the polygons not reflected in differences

in zonal statisticsin zonal statistics Stand median acreage = 5.25Stand median acreage = 5.25 Segmentation median acreage = 2.75Segmentation median acreage = 2.75

Preliminary findingsPreliminary findings Stand and segmentation polygons seem to be almost Stand and segmentation polygons seem to be almost

equally homogeneous, with a slight edge to the latterequally homogeneous, with a slight edge to the latter Average size of the polygons not reflected in differences Average size of the polygons not reflected in differences

in zonal statisticsin zonal statistics Stand median acreage = 5.25Stand median acreage = 5.25 Segmentation median acreage = 2.75Segmentation median acreage = 2.75


Future workFuture workFuture workFuture work

Phase I Phase I AnalysisAnalysis

• Generate texture images using GLCMGenerate texture images using GLCM• Calculate zonal statistics for stand and Calculate zonal statistics for stand and segmentation polygonssegmentation polygons

Phase II Phase II AnalysisAnalysis

• Statistical analysis to characterize Statistical analysis to characterize differences in homogeneity, if anydifferences in homogeneity, if any• Relate texture metrics to characteristics of Relate texture metrics to characteristics of modeling unitsmodeling units

Follow-onFollow-on • Comparison of modeling units to field dataComparison of modeling units to field data• Run and test forest structure modelsRun and test forest structure models

Richard M. WarnickRichard M. Warnick

[email protected]@fs.fed.us

Richard M. WarnickRichard M. Warnick

[email protected]@fs.fed.us

Documents

Texture Metric Comparison of Manual Forest Stand Delineation and Image Segmentation Richard M. Warnick, Ken Brewer, Kevin Megown, Mark Finco, and Brian