Canopy Texture Mapping

by Megan Kanaga Creutzburg

Other Names:

Texture analysis
Image texture analysis


Texture is the spatial distribution of tones across the pixels of remotely sensed images, providing a measure of tonal variability. Texture analysis or texture mapping is a common method for delineating surface features that cause localized variations in the brightness and other spectral properties of the satellite image, including shadowing. Texture can be used as an important descriptor of ecological systems by approximating vegetation heterogeneity and other ecological indicators. Local variation in spectral properties is measured within a window of a particular size that is passed across the image to assign each pixel a texture value based on the spectral variability of its neighbors.

The images below show an aerial photo of a forested landscape (left) and a texture analysis of the photo (right). The circles identify an area where texture is low (low complexity and variability in spectral composition of neighboring pixels – left circles) and where texture is high (high complexity and heterogeneity – right circles). In this case, the left part of the figure shows an early-successional even-aged stand, and the right part of the figure shows how canopy complexity and heterogeneity increase with successional stage (image analysis performed by Jason Karl from a 2004 NAIP image).

Chica-Olmo & Abarca-Hernández (2000) identify three major categories of texture processing algorithms: structural, spectral and statistical. Structural methods produce texture values by using repetition of primitive patterns with certain rules of placement, but do not handle irregular patterns well. Spectral methods are based on the Fourier transform, a technique for separating an image into its spatial frequency components. Statistical methods include models of statistical properties such as fractal dimension, autocorrelation, and co-occurrence, and are used most frequently in landscape ecology. Gray level co-occurrence matrix (GLCM) texture analysis is one of the more commonly used statistical methods to describe variation in gray scale values in a local area, although it is extremely computationally intensive to calculate. One of the easiest and simplest methods to assess image texture is simply using the standard deviation of neighboring pixels in a moving window analysis across an image. In the case of object-based image analysis, the standard deviation of reflectance values across all the pixels in the object provides a good measure of texture. Texture analysis can also be performed using digitized historical aerial photographs, and therefore can be useful for assessing changes in vegetation communities from historic states.

Similar Methods


Texture analysis produces a map delineating the distribution of vegetation with particular textural properties.

Successful Rangeland Uses

Texture analysis has typically been used to characterize forest ecosystems, but some applications to other ecosystems include:

  • Ge et al. (2006) used texture analysis to map invasive Tamarisk populations along a riparian corridor.
  • Guo et al. (2004) and Zhang et al. (2006) assessed grassland heterogeneity using texture analysis
  • Hudak & Wessman (1998) used texture analysis to examine historical encroachment of woody plants into a savanna ecosystem
  • Strand et al (2008) characterized carbon accumulation rates due to juniper encroachment in arid landscapes using texture analysis and other techniques

Application References

  • Ge, S., R. Carruthers, P. Gong, and A. Herrera. 2006. Texture analysis for mapping Tamarix parviflora using aerial photographs along the Cache Creek, California. Environmental Monitoring and Assessment 114: 65-83.
  • Guo, X. J. Wilmshurst, S. McCanny, P. Fargey, and P. Richard. 2004. Measuring spatial and vertical heterogeneity of grasslands using remote sensing techniques. Journal of Environmental Informatics 3:24-32.
  • Hudak, A.T. and C.A. Wessman. 1998. Textural Analysis of Historical Aerial Photography to Characterize Woody Plant Encroachment in South African Savanna. Remote Sensing of Environment 66: 317-330.
  • Strand, E.K., L.A. Vierling, A.M.S. Smith, and S.C. Bunting. 2008. Net changes in aboveground woody carbon stock in western juniper woodlands, 1946-1998. Journal of Geophysical Research 113: G01013. DOI: 10.1029/2007JG000544.
  • Zhang, C., X. Guo, J. Wilmshurst, and R. Sissons. 2006. Application of RADARSATimagery to grassland biophysical heterogeneity assessment. Canadian Journal of Remote Sensing 32: 281-287.

Technical References

  • Chica-Olmo, M. and F. Abarca-Hernández. 2000. Computing geostatistical image texture for remotely sensed data classification. Computers & Geosciences 26: 373-383.
  • Ferro, C.J.S. 1998. Scale and texture in digital image classification. Thesis, West Virginia University, Morgantown, WV. Available online at
  • Franklin, S. E., Wulder, M. A. and Gerylo, G. R. 2001. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. International Journal of Remote Sensing 22: 2627-2632.
  • Haralick, R.M. 1979. Statistical and Structural Approaches to Texture. Proceedings of the IEEE 67: 786-804.
  • Livens, S., P. Scheunders, G. van de Wouwer, and D. Van Dyck. 1997. Wavelets for texture analysis, an overview. IPA97, 15-17 July 1997, Conference Publication No. 443.
  • Smith, A.M.S., M.J. Wooster, A.K. Powell, and D. Usher. 2002. Texture based feature extraction: application to burn scar detection in Earth Observation Imagery. International Journal of Remote Sensing. 23: 1733-1739.
  • Solomon, D.Z., H.D. Safford, Y.B. Chen, and S.L. Ustin. 2008. Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification. Applied Vegetation Science 11: 499-508.
  • Tuceryan, M. and A.K. Jain. 1998. Chapter 2.1: Texture Analysis. In: The Handbook of Pattern Recognition and Computer Vision (2nd Edition). Chen, C. H., L. F. Pau, P. S. P. Wang, editors. World Scientific Publishing Co, pp. 207-248.
  • Zhou, D. 2006. Texture Analysis and Synthesis using a Generic Markov-Gibbs Image Model. Thesis, University of Auckland, NZ. Available online at


The usefulness of texture analysis will depend on the appropriateness of a particular technique or algorithm to the study site, the level of differentiation of textural properties between disparate vegetation types, and the scale at which textural measurements are taken. See Ferro (1998) for a discussion of scale in texture analysis, and refer to Strand et al. (2008) for an example of texture analysis overestimating juniper cover due to similar textural properties of juniper compared to other vegetation types.

Data Inputs

Data inputs vary based on the method of texture analysis used.

Software/Hardware Requirements

Texture analysis requires image processing and statistical/mathematical modeling software.

Sample Graphics

Three methods of texture analysis of image objects in the Castle Creek area of southwestern Idaho, including standard deviation and two measures from the gray level co-occurrence matrix (GLCM) method (texture analysis performed by Jason Karl from a 2008 Ikonos image).

Aerial photographs showing change in juniper cover from 1946 to 1998 in southwestern Idaho. Left images are aerial photos from the two years (a and d); middle images show juniper crown diameters in white, estimated by wavelet analysis (b and e); and right images show estimated juniper cover in white based on texture analysis (c and f). In this study, texture analysis overestimated juniper cover due to similar textural properties of juniper and other vegetation types (source: Strand et al. 2008).

Additional Information

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