Application strategies for GLCM textures from very high spatial resolution optical imagery over subtropical plantations

Li Ronghui; Chen Ling; Wu Mingjing; Yu Xiaolong; Zhao Xiuhai

doi:10.12171/j.1000-1522.20200139

Journal of Beijing Forestry University > 2021 > 43(1): 1-9. > DOI: 10.12171/j.1000-1522.20200139

Li Ronghui, Chen Ling, Wu Mingjing, Yu Xiaolong, Zhao Xiuhai. Application strategies for GLCM textures from very high spatial resolution optical imagery over subtropical plantations[J]. Journal of Beijing Forestry University, 2021, 43(1): 1-9. DOI: 10.12171/j.1000-1522.20200139

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Application strategies for GLCM textures from very high spatial resolution optical imagery over subtropical plantations

1.
Key Laboratory of Forest Resources & Environmental Management of National Forestry and Grassland Administration, Research Center of Forest Management Engineering of National Forestry and Grassland Administration,Beijing Forestry University, Beijing 100083, China
2.
Fujian Jiangle State-Owned Forest Farm, Sanming 353300, Fujian, China

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Received Date: May 10, 2020
Revised Date: June 15, 2020
Available Online: December 09, 2020
Published Date: February 04, 2021

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Abstract

Abstract

Objective This paper aims to discuss textural variables and their corresponding input parameters, and clarify a specific and simple strategy for the optimal utilization of textural information from very high spatial resolution (VHR) imagery in the field of forestry.
Method Based on a case of Chinese fir plantation with different age classes under varied topographic conditions from Jiangle State-Owned Forest Farm of Fujian Province of eastern China, seven gray level co-occurrence matrix (GLCM) textures and four input parameters were involved to demonstrate the optimal selection of textures and the setting of input parameters.
Result Almost all textures had higher age class separability over shady slope than sunny slope, and both the measurement index and the correlation between different textures should be used to select the optimal textures. Among the four input parameters, moving window size was the most important for textures in statistic and orderliness groups. Optimal window sizes should be jointly determined by the spatial resolution of images and spatial scale of research objects. Textures in contrast group were independent of window size, which means that any window size can be set for those textures. No attention need to be paid to the setting of displacement for textures in statistic and orderliness groups, but the situation was different for textures in contrast group. Moreover, orientation setting should be more concerned with the increase of displacement for textures in statistic group, but not for textures in the other two groups. As for the most neglected parameter, the grayscale quantization level can be set to 32 or 64.
Conclusion The textural information of VHR imagery can be used to distinguish objects with high spectral overlaps, which can “make up” the spectral information loss caused by shadows. However, textural variables and their input parameters should be optimized. The specific strategy and all the general rules from this study can provide practical suggestions for the optimal utilization of textural information from VHR imagery.
- QuickBird,
- GLCM texture,
- optimization of textural variable,
- optimal parameter setting

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References (24)

References

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Application strategies for GLCM textures from very high spatial resolution optical imagery over subtropical plantations