Texture Metric Comparison of Manual Forest Stand Delineation and Image Segmentation Richard M. Warnick, Ken Brewer, Kevin Megown, Mark Finco, and Brian Schwind USDA Forest Service Remote Sensing Applications Center Ralph Warbington USDA Forest Service Pacific Southwest Region Jim Barber USDA Forest Service Northern Region RS 2006 April 26, 2006 Richard M. Warnick, Ken Brewer, Kevin Megown, Mark Finco, and Brian Schwind USDA Forest Service Remote Sensing Applications Center Ralph Warbington USDA Forest Service Pacific Southwest Region Jim Barber USDA Forest Service Northern Region RS 2006 April 26, 2006

USDA Forest Service, Remote Sensing Applications Center, IntroductionIntroduction Cornerstone assumption: Stand-level forest models work best with homogeneous units Cornerstone assumption: Stand-level forest models work best with homogeneous units Is there an overall difference in textural homogeneity between manual stand delineation and image segmentation polygons for the same study area? Is there an overall difference in textural homogeneity between manual stand delineation and image segmentation polygons for the same study area? Cornerstone assumption: Stand-level forest models work best with homogeneous units Cornerstone assumption: Stand-level forest models work best with homogeneous units Is there an overall difference in textural homogeneity between manual stand delineation and image segmentation polygons for the same study area? Is there an overall difference in textural homogeneity between manual stand delineation and image segmentation polygons for the same study area? STAND BOUNDARIES IMAGE SEGMENTATION

USDA Forest Service, Remote Sensing Applications Center, IntroductionIntroduction Utilize texture metrics Utilize texture metrics  Gray-Level Co-occurrence Matrix (GLCM) metrics  Correlation  Entropy  Mean  Variance Utilize texture metrics Utilize texture metrics  Gray-Level Co-occurrence Matrix (GLCM) metrics  Correlation  Entropy  Mean  Variance Compare manual interpretation and image segmentation for forest stand delineation Compare manual interpretation and image segmentation for forest stand delineation  Vertical stereo photo interpretation  eCognition™ image segmentation from Landsat ETM+ Compare manual interpretation and image segmentation for forest stand delineation Compare manual interpretation and image segmentation for forest stand delineation  Vertical stereo photo interpretation  eCognition™ image segmentation from Landsat ETM+

USDA Forest Service, Remote Sensing Applications Center, Study area Idaho Panhandle National Forests, Idaho/Montana/Washington Study area covers most of Kaniksu NF Idaho Panhandle National Forests, Idaho/Montana/Washington Study area covers most of Kaniksu NF IDAHO

USDA Forest Service, Remote Sensing Applications Center, Study area Study area Landsat view, Idaho/Montana

USDA Forest Service, Remote Sensing Applications Center, GIS datasets Idaho Panhandle National Forests stand boundaries Idaho Panhandle National Forests stand boundaries  Vertical stereo photography ( )  Manual stand delineation (1980s – 1990s) Northern Region Vegetation Mapping Project Northern Region Vegetation Mapping Project  Landsat ETM+ July/August 2002  Image segmentation using eCognition™ Idaho Panhandle National Forests stand boundaries Idaho Panhandle National Forests stand boundaries  Vertical stereo photography ( )  Manual stand delineation (1980s – 1990s) Northern Region Vegetation Mapping Project Northern Region Vegetation Mapping Project  Landsat ETM+ July/August 2002  Image segmentation using eCognition™

USDA Forest Service, Remote Sensing Applications Center, ImageryImagery Landsat ETM+ panchromatic band (15 m) Landsat ETM+ panchromatic band (15 m)  2002 image subset to project area IRS 1-C panchromatic (5 m) IRS 1-C panchromatic (5 m)  1998 image subset to project area DOQQ mosaic (1 m) DOQQ mosaic (1 m)  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 county mosaic subset to project area  First principal component image generated to reduce image to one band Landsat ETM+ panchromatic band (15 m) Landsat ETM+ panchromatic band (15 m)  2002 image subset to project area IRS 1-C panchromatic (5 m) IRS 1-C panchromatic (5 m)  1998 image subset to project area DOQQ mosaic (1 m) DOQQ mosaic (1 m)  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 county mosaic subset to project area  First principal component image generated to reduce image to one band

USDA Forest Service, Remote Sensing Applications Center, Texture metrics Selection of texture measures Relatively simple and repeatable Relatively simple and repeatable Can be run on commercial software (ENVI ® ) Can be run on commercial software (ENVI ® ) Adaptable to corporate software (ERDAS Imagine) Adaptable to corporate software (ERDAS Imagine) Technique common in remote sensing literature Technique common in remote sensing literature Need four texture measures, not highly correlated Need four texture measures, not highly correlated Relatively simple and repeatable Relatively simple and repeatable Can be run on commercial software (ENVI ® ) Can be run on commercial software (ENVI ® ) Adaptable to corporate software (ERDAS Imagine) Adaptable to corporate software (ERDAS Imagine) Technique common in remote sensing literature Technique 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 Gray-level Co-occurrence Matrix (GLCM) GLCM texture tutorial by Mryka Hall-Beyer

USDA Forest Service, Remote Sensing Applications Center, Texture metrics GLCM Correlation GLCM Correlation  Measures the linear dependency of gray levels on those of neighboring pixels in the GLCM GLCM Entropy GLCM Entropy  Measures the level of spatial disorder of gray levels in the GLCM GLCM Mean GLCM Mean  Measures the mean of the probability values from the GLCM GLCM Variance GLCM Variance  Measures the dispersion around the mean of combinations of reference and neighbor pixels in the GLCM GLCM Correlation GLCM Correlation  Measures the linear dependency of gray levels on those of neighboring pixels in the GLCM GLCM Entropy GLCM Entropy  Measures the level of spatial disorder of gray levels in the GLCM GLCM Mean GLCM Mean  Measures the mean of the probability values from the GLCM GLCM Variance GLCM Variance  Measures the dispersion around the mean of combinations of reference and neighbor pixels in the GLCM Gray-Level Co-occurrence Matrix texture metrics

USDA Forest Service, Remote Sensing Applications Center, Texture metrics 3 x 3 moving window

USDA Forest Service, Remote Sensing Applications Center, Texture metrics Image pixel DN Sample window

USDA Forest Service, Remote Sensing Applications Center, Texture metrics Neighbor pixel DN Reference pixel DN GLCM for sample window Distance from the diagonal is proportional to the amount of contrast

USDA Forest Service, Remote Sensing Applications Center, Image processing ENVI ® 4.2 used to generate GLCM texture images ENVI ® 4.2 used to generate GLCM texture images  GLCM correlation  GLCM entropy  GLCM mean  GLCM variance ENVI ® 4.2 used to generate GLCM texture images ENVI ® 4.2 used to generate GLCM texture images  GLCM correlation  GLCM entropy  GLCM mean  GLCM variance CORRELATIONENTROPYMEANVARIANCE

USDA Forest Service, Remote Sensing Applications Center, Image processing M – mosaic S – subset PC – principal component T – GLCM texture

USDA Forest Service, Remote Sensing Applications Center, Zonal statistics

USDA Forest Service, Remote Sensing Applications Center, Zonal statistics

USDA Forest Service, Remote Sensing Applications Center, Zonal statistics

USDA Forest Service, Remote Sensing Applications Center, Zonal statistics

USDA Forest Service, Remote Sensing Applications Center, Zonal statistics Distribution of zonal means GLCM variance with NAIP STAND POLYGONS SEGMENTATION POLYGONS Mean = Std Dev = N = 8,159 Mean = Std Dev = N = 33,468

USDA Forest Service, Remote Sensing Applications Center, ConclusionsConclusions Hypotheses Hypotheses  Segmentation polygons would be more homogeneous in texture  Represent existing vegetation instead of management units  Polygons with smaller area are more homogeneous  Texture differences may be minimized Hypotheses Hypotheses  Segmentation polygons would be more homogeneous in texture  Represent existing vegetation instead of management units  Polygons with smaller area are more homogeneous  Texture differences may be minimized Preliminary findings Preliminary findings  Stand and segmentation polygons seem to be almost equally homogeneous, with a slight edge to the latter  Average size of the polygons not reflected in differences in zonal statistics  Stand median acreage = 5.25  Segmentation median acreage = 2.75 Preliminary findings Preliminary findings  Stand and segmentation polygons seem to be almost equally homogeneous, with a slight edge to the latter  Average size of the polygons not reflected in differences in zonal statistics  Stand median acreage = 5.25  Segmentation median acreage = 2.75

USDA Forest Service, Remote Sensing Applications Center, Future work Phase I Analysis Generate texture images using GLCM Generate texture images using GLCM Calculate zonal statistics for stand and segmentation polygons Calculate zonal statistics for stand and segmentation polygons Phase II Analysis Statistical analysis to characterize differences in homogeneity, if any Statistical analysis to characterize differences in homogeneity, if any Relate texture metrics to characteristics of modeling units Relate texture metrics to characteristics of modeling units Follow-on Comparison of modeling units to field data Comparison of modeling units to field data Run and test forest structure models Run and test forest structure models

Richard M. Warnick