Multi‐layer Forest Stand Discrimination with Spatial Co‐occurrence Texture Analysis of High Spatial Detail Airborne Imagery

Abstract

Three spatial resolutions of airborne remote sensing imagery (60 cm, 1 m, and 2 m) collected over multi-layer aspen, pine, spruce, and mixedwood forest stands in Alberta on July 18 th , 1998 were tested for their ability to provide a statistical stand discrimination based on spatial co-occurrence texture analysis. As spatial resolution increased, classification accuracies increased. The highest classification accuracy of 86.7% was obtained using the highest image spatial resolution data (60 cm), with spatial co-occurrence texture and spectral signatures combined, and a thirteen-class multi-layer stand stratification. The texture of the highest spatial resolution imagery (60 cm pixel resolution) was interpreted to contain information on the crown architecture of individual trees. In larger windows, the texture was interpreted to contain information on stand structure. Texture of lower spatial resolution imagery (1 m and 2 m pixel resolution) could not detect individual tree crown architecture and was determined to be related primarily to stand structure characteristics. The use of texture channels improved the per-plot classification accuracies by 15.7%, compared to the use of the spectral data alone.

Monitoring Forest Recovery Following Wildfire and Harvest in Boreal Forests Using Satellite Imagery

“…For example, [52] found low accuracy classifying from Landsat images of mixed stands, and [53] found that stand age and height influence the overall canopy and understory reflectance values. Adding textural information during the classification process can improve classification accuracy by 12% or more [54]. Among remote sensing analysis methods, geographic object-based image analysis is considering a promising approach [55,56].…”

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“…Additional features improved the overall classification including: the all adjacent pixels, Grey Level Co-occurrence Matrix (GLCM) homogeneity texture [29] which was used to help further subdivide vegetation class types, 'Proximity to other classes' and 'Shared relative border to other classes', which helped improve individual classes accuracy. All classes were exported into an ArcGIS geodatabase and post-processed, including manual clean-up and removal of image scene lines through merging polygons.…”

“…We completed the 2002 classification by utilizing a few additional features to improve the overall classification: all adjacent pixels, GLCM homogeneity texture [29] was used to help distinguish vegetation class types, 'Length to Width' helped classify linear features such as grass medians and long streets, 'Area of objects' and 'Proximity to other classes' each helped separate all classes [28]. All classes were exported into an ArcGIS geodatabase and post-processed.…”

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“…Carr and Pellon de Miranda [35] found that second-order texture variables outperformed all other texture measures tested, including the semivariance texture used by Wulder et al [36,37] in a leaf area index texture analysis. Second order texture has been demonstrated to be successful in conventional per-pixel image analysis [38][39][40], but in OBIA the texture feature needs special processing to generate, thus, limited examples of texture usage has been demonstrated in general forest type mapping [41,42]. Continuing work on image texture applications in OBIA is needed and should be aimed at generating a more complete understanding of texture and the conditions under which texture can contribute to classification of forests.…”