Abstract
The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.
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Authors would like to greatly thank and acknowledge the reviewers of this paper for giving their time. Their precious advices and suggestions improved the quality of the present paper.
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Project funding: This work was supported by the National Natural Science Youth Foundation of China (No. 41501283), the Fundamental Research Funds for the Central Universities (2015ZCQ-GX-04).
The online version is available at http://www.springerlink.com
Corresponding editor: Chai Ruihai.
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Zhao, Y., Han, Q., Zhao, Y. et al. Soil pore identification with the adaptive fuzzy C-means method based on computed tomography images. J. For. Res. 30, 1043–1052 (2019). https://doi.org/10.1007/s11676-018-0725-3
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DOI: https://doi.org/10.1007/s11676-018-0725-3