Multidimensional statistical methods for classification and diagnostics of the pore space in micromorphological thin sections

The methods of multidimensional statistics and claster analysis were used to create an automated classification and diagnostics of the pore space in 200 micromorphological vertically oriented thin sections prepared from podzolic soils (the Republic of Komi, Russia), soddy-podzolic soils in the Moscow region, gray forest soils (Tula region) and chernozems (Kursk and Voronezh regions in Russia, Odessa region in Ukraine). The computer imagination analysis permitted to study fine macro-voids ( d = 0.2-2 mm) in the field of vision 2x2 cm. Every void in the field of vision was measured to determine its section area ( S ), perimeter ( P ), diametrical ( D ) and longitudinal ( L ) sections, orientation of the long section axe in the plane thin section and the form factor F = (4 pS / P² + D / L )/2. 100-150 voids were taken for measurements in every thin section. To characterize the pore space in thin sections, under use were also the empiric distribution of voids according to the form factor and orientation as well as the average length of voids in the field of vision. The discrimination analysis made it possible to elaborate an automated system and to give the morphometric characteristics of the pore space in the studied soils with varying structures so specific for loamy soils at the territory of European Russia including crumbly, granular, nutty, platy, massive-platy, fissure-like and massive structures. The results of the automated diagnostics have a rather high similarity with the expert visual assessment of the pore space in thin sections (75-90%).

поровое пространство почвы, классификация, диагностика, многомерные статистические методы, кластерный анализ, the pore space in soil, classification, diagnostics, multidimensional statistic methods, claster analysis

1. Герасимова М.И. Микроморфологическая диагностика и микроморфотипы почв: Автореф. … дис. д. б. н. М., 1992. 43 с.

2. Герке К.М., Карсанина М.В., Скворцова Е.Б. Описание и реконструкция строения порового пространства почвы с помощью корреляционных функций // Почвоведение. 2012. № 9. С. 962.

3. Рожков В.А. Почвенная информатика. М.: Агропромиздат, 1989. 200 с.

4. Рожков В.А. Формальный аппарат классификации почв // Почвоведение. 2011. № 12. С. 1411-1424.

5. Рожков В.А., Скворцова Е.Б. Тектология почвенной мегасистемы (общность организации и анализа данных) // Почвоведение. 2009. № 10. С. 1155-1164.

6. Скворцова Е.Б., Калинина Н.В. Микроморфометрические типы строения порового пространства целинных и пахотных суглинистых почв // Почвоведение. 2004. № 9. С. 1114-1125.

7. Burrough P.A. Fractals in soil science // European J. Soil Sci. 2001. Т. 52. № 3. С. 527-528.

8. Cárdenas J.P., Santiago A., Losada J.C., Benito R.M., Tarquis A.M., Borondo F. Soil porous system as heterogeneous complex network // Geoderma. 2010. Т. 160. № 1. С. 13-21.

9. Hartemink A.E., Minasny B. Towards digital soil morphometrics // Geoderma. 2014. Т. 230-231. С. 305-317.

10. Holden N.M. Description and classification of soil structure using distance transform data // European J. Soil Sci. 2001. Т. 52. № 4. С. 529-545.

11. Ringrose-Voase A.J. A scheme for the quantitative description of soil macrostructure by image analysis // J. Soil Sci. 1987. V. 38. P. 343-356.