Rainfall interception (RI) by forest canopies is an important process in hydrological cycling in forest ecosystems. However, accurately predicting RI is a challenging topic. In this study, a dimensionless descriptor, canopy interception index (CII), for predicting RI was defined. The terrestrial laser scanning was used to estimate CII in four temperate forest types, including Korean pine (Pinus koraiensis) plantation forest (KPF) stands, larch (Larix spp.) plantation forest (LPF) stands, mixed broadleaved forest (MBF) stands and Mongolian oak (Quercus mongolica) forest (MOF) stands. Using the measured RI values over the rainy seasons in 2017 and 2018, CII's performance for predicting RI was tested and also compared with several other indices (LAI: leaf area index, PAI: plant area index and ACH: average canopy height). The results indicated that CII was significantly and strongly related with RI for the four forest types together (R² = 0.79), as well as for an individual forest type (R² = 0.55–0.63). More importantly, its performance was better than those from LAI (R² = 0.33–0.43), PAI (R² = 0.40–0.53) and ACH (R² = 0.35). All those results demonstrated that CII was an efficient index for accurately predicting RI. The potential applications of CII were also discussed.

中文翻译：

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地面激光扫描法冠层截留指数用于预测降雨截留

森林冠层截留降雨（RI）是森林生态系统中水文循环的重要过程。但是，准确预测RI是一个具有挑战性的话题。在这项研究中，定义了用于预测RI的无量纲描述符，冠层截留指数（CII）。陆地激光扫描用于估算四种温带森林类型的CII，包括红松（Pinus koraiensis）人工林（KPF）林分，落叶松（Larix spp。）人工林（LPF）林分，混合阔叶林（MBF）林分和蒙古栎（蒙古栎））森林（MOF）站立。使用2017年和2018年雨季的测得RI值，对CII预测RI的性能进行了测试，并将其与其他几个指标（LAI：叶面积指数，PAI：植物面积指数和ACH：平均冠层高度）进行了比较。结果表明，对于四种森林类型（R ² = 0.79）以及单个森林类型（R ² = 0.55-0.63），CII与RI显着且强烈相关。更重要的是，其性能优于LAI（R ² = 0.33–0.43），PAI（R ² = 0.40–0.53）和ACH（R ²= 0.35）。所有这些结果表明，CII是准确预测RI的有效指标。还讨论了CII的潜在应用。