[Rainfall redistribution traits of three main forest types in Dagangshan Mountains of Jiangxi Province, China].

The redistribution processes of rainfall due to the canopy were studied on three typical forest types (Chinese fir forest, evergreen broad-leaved forest and Phyllostachys pubescens forest) in Dagangshan Mountains of Jiangxi Province. The results showed that from April to June, 2012, the total precipitation was 531.6 mm, with the maximum single rainfall of 61.7 mm. The rainfall in this area was mainly light and moderate. During the research period, the total throughfall of P. pubescens forest was the greatest, and that of evergreen broad-leaved forest was the smallest. The throughfall of P. pubescens and Chinese fir forest were almost equal at the same rainfall intensity. However, the throughfall of evergreen broad-leaved forest was smaller than those of the other two types of forest at the same high rainfall intensity. Throughfall presented a distinct spatial variability within each forest. Stemflow of Chinese fir forest, evergreen broad-leaved forest and P. pubescens forest were 1.4%, 8.9% and 8.8%, respectively. There were significant differences (P < 0.01) in stemflow between the Chinese fir forest and the other two types of forests. In addition, the moisture degree of forests before a rain event greatly influenced the quantity of the stemflow. The effect was strongest in the Chinese fir plantation and weakest in the P. pubescens forest. The proportion of interception to rainfall was in a descending order of 30.5%, 25.5% and 19.2% for the Chinese fir forest, the evergreen broad-leaved forest and the P. pubescens forest, respectively. The Chinese fir forest had the obviously greater interception rate than the other two types of forests under usual rainfall in the study area.

[Estimation of forest canopy closure by using partial least square regression].

Based on remote sensing and forest resources inventory data, this paper approached the feasibility of using Bootstrap approach to select optimal variables and using partial least square (PLS) regression to build a model for estimating forest canopy closure. The results showed that whether using a model built with all variables or a model with the optimal variables selected by Bootstrap approach, the relative deviation in estimating forest canopy closure was about 5%. The optimal variables selected in this paper differed greatly with those in the studies for other areas, suggesting that besides selection method, zonal vegetation and terrain could also induce the differences of selected optimal variables for the estimation of forest canopy closure.