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The purpose of the present study was to evaluate the applicability of three methods of texture analysis (Spatial Gray Level Dependence Method: SGLDM, Gray Level Run Length Method: GLRLM and Power Spectrum Method: PSM) to photographic images from close distance. These methods have been applied primarily to satellite images. For an example, a 1m×1m ground surface covered by a mixture of orchard grass and white clover was photographed, and the photographic image was divided into 49 square blocks. The composition of the 2 species was estimated for each block.The image of the mixed pasture was characterized by the mixed texture of the 2 species with very different leaf shapes and by the heterogeneous texture due to the roughness of the canopy surface.Most of the textural features related fairly well to the composition of the 2 species for all the three different methods. They, however, showed a significant difference in stability and linearity.In the estimation of a dominant species, classification accuracy exceeded 80% in all the methods. In a comparison between the observation and estimation of the species composition, the coefficient of correlation exceeded 0.8. In particular, GLRLM showed the highest value of 0.918 among the three methods. Standard error of estimation was 11.6% in GLRLM, 16.0% in SGLDM and 16.7% in PSM

Texture Analyses of Photographic Images from Close Distance: An Application to Estimate Species Composition in a Mixed Pasture Field.