Organochlorine pesticides (OCPs) have been a major environmental issue, attracting much scientific concern because of their nature of toxicity, persistence, and endocrine disrupting effects. Soil samples were collected from ten college school yards in Beijing in 2006 and analyzed to determine fifteen OCPs. Dichlorodiphenyltrichloroethanes (DDTs) were found to be the main pollutants, accounting for 93.70% of total OCPs, followed by hexachlorohexanes (HCHs) (2.25%) and hexachlorobenzene (HCB) (1.82%). Content of chlordanes (CHLs), heptachlors (HEPTs), and endosulfans comprised 0.51%, 1.05%, and 0.79% of fifteen OCPs, respectively. The preliminary pollution assessment indicated that DDTs have caused high OCPs levels in some schools. Source identification showed that HCHs in soils were originated from an old mixed source of technical HCHs and lindane. And DDTs were mainly from mixed use of technical DDTs and dicofol containing DDT impurities. According to GB15618-1995 (guidelines of Chinese environmental quality standards for soils), HCHs and DDTs levels might be categorized as little and low polluting pesticides. This study indicated that the environmental quality of college school yards with large green land were not as good as was expected and there existed potential exposure risk of college population to OCPs.

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Estimating clumping indices is important for determining the leaf area index (LAI) of forest canopies. The spatial distribution of the clumping index is vital for LAI estimation. However, the neglect of woody tissue can result in biased clumping index estimates when indirectly deriving them from the gap probability and LAI observations. It is difficult to effectively and automatically extract woody tissue from digital hemispherical photos. In this study, a method for the automatic detection of trunks from Terrestrial Laser Scanning (TLS) data was used. Between-crown and within-crown gaps from TLS data were separated to calculate the clumping index. Subsequently, we analyzed the gap probability, clumping index, and LAI estimates based on TLS and HemiView data in consideration of woody tissue (trunks). Although the clumping index estimated from TLS had better agreement (R 2 = 0.761) than that from HemiView, the change of angular distribution of the clumping index affected by the trunks from TLS data was more obvious than with the HemiView data. Finally, the exclusion of the trunks led to a reduction in the average LAI by~19.6% and 8.9%, respectively, for the two methods. These results also showed that the detection of woody tissue was more helpful for the estimation of clumping index distribution. Moreover, the angular distribution of the clumping index is more important for the LAI estimate than the average clumping index value. We concluded that woody tissue should be detected for the clumping index estimate from TLS data, and 3D information could be used for estimating the angular distribution of the clumping index, which is essential for highly accurate LAI field measurements.