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This research was conducted in Khanikan forests located in lowland of Mazandaran province (North of Iran). Eighteen profiles were dug and several chemical, physical and biological soil properties were investigated. The soil properties evaluated were soil pH, bulk density, saturation moisture content, electrical conductivity, organic carbon, total nitrogen, cation exchangeable capacity, available phosphorous, soil texture, calcium carbonate content, number and biomass of earthworms, litter carbon and litter nitrogen. Principal Component Analysis (PCA) was used to identify the variation of soil properties. PCA, a technique which reduces the dimensionality of multivariate data by removing Interco relations among variables, has a number of useful applications in forest researches. The results showed significant relationships between some soil factors with PC1 and PC2 axes, also, among different soil factors, the distribution of forest types was most strongly controlled with some soil characteristics such as acidity, bulk density, texture, phosphorous, organic carbon, total nitrogen and cation exchangeable capacity.

A TWINSPAN classification of 60 sample plots from the Khanikan forest (North of Iran) is presented. Plant types were determined from field observations and sample plot data arranged and analyzed in association tables. The types were defined on the basis of species patterns of presence, absence and coverage values. Vegetation was sampled with randomized-systematic method. Vegetation data including density and cover percentage were estimated quantitatively within each quadrate and using the two-way indicator species analysis. The objectives of the study were to plant type's classification for Khanikan lowland forest in North of Iran, Identification of indicator species in plant types and increase our understanding in regarding to one of Multivariate analysis methods (TWINSPAN). Five plant types were produced for the study area by TWINSPAN, i.e., Menta aquatica, Oplismenus undulatifolius, Carex grioletia, Viola odarata and Rubus caesius. Therefore, at each step of the process, the program identifies indicator species that show strongly differential distributions between groups and so can severe to distinguish the groups. The final result, incorporating elements of classification can provide a compact and powerful summary of pattern in the data set.