Terrestrial laser scanning-derived canopy interception index for predicting rainfall interception
Terrestrial laser scanning-derived canopy interception index for predicting rainfall interception
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地面激光扫描得出的冠层拦截指数用于预测降雨拦截
DOI：
10.1002/eco.2212
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发表时间：
2020
期刊：
影响因子：
2.6
通讯作者：
Li Mingcai
中科院分区：
文献类型：
--
作者：
Yu Yue;Gao Tian;Zhu Jiaojun;Wei Xiaohua;Guo Qinghua;Su Yanjun;Li Yumei;Deng Songqiu;Li Mingcai
林冠截留是森林生态系统水文循环的重要过程。然而，准确预测RI是一个具有挑战性的课题。在这项研究中，一个无量纲的描述，冠层截留指数（CII），用于预测RI的定义。利用陆地激光扫描技术，对红松种植园林、落叶松人工林、杉木人工林和杉木人工林4种温带森林类型的CII进行了测定。种植园林（LPF）林分、阔叶混交林（MBF）林分和蒙古栎（Quercus mongolica）林（MOF）林分。利用2017年和2018年雨季的实测RI值，对CII预测RI的性能进行了测试，并与其他几个指标（LAI：叶面积指数，派：植物面积指数和ACH：平均冠层高度）进行了比较。结果表明，四种林型的CII与RI均呈显著的强相关（R2= 0.79），而单个林型的CII与RI也呈显著的强相关（R2= 0.55-0.63）。更重要的是，其性能优于LAI（R2= 0.33-0.43）、派（R2= 0.40-0.53）和ACH（R2= 0.35）。这些结果表明，CII是准确预测RI的有效指标。最后讨论了CII的应用前景。
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.