首页> 外文会议>IEEE International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications >Seasonal leaf area index variations derived from needle growth and fall measurements in two eastern white pine (Pinus Strobes L.) stands
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Seasonal leaf area index variations derived from needle growth and fall measurements in two eastern white pine (Pinus Strobes L.) stands

机译:两种东部白松(Pinus Strobes L.)的针头生长和秋季测量结果衍生的季节叶面积指数变化

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Existing global leaf area index (LAI) products have a common problem of too large seasonal variations for conifer stands. The seasonal variation of remotely sensed LAI is often distorted due to many confounding factors. We propose a methodology to quantify the LAI seasonal pattern based on needle lifespan and meteorological conditions. In this paper, we test this methodology in two conifer stands in Ontario, Canada. Needle growth and senescence rates were quantified from needle elongation measurements and litterfall collection. By measuring the average needle lifespan and assuming a dynamic equilibrium of LAI for the full annual cycle, the amount of needles renewed annually can be estimated. Then it is seasonally ‘distributed’ based on these two rates in order to reckon seasonal variations of new and old needle cohorts. These measured LAI seasonal trajectories were compared with combined measurements by TRAC (Tracing Radiation and Architecture of Canopies) and LAI 2000 plant canopy analyzer. The difference between peak LAI from needle growth and fall and that from optical instruments is constrained within 15%. Meanwhile, LAI values from needle growth and fall beyond the growing season are not fallaciously low like LAI from remote sensing data, resulting in a more realistic seasonal variation of needles than that is retrieved from algorithms using remote sensing data. We believe this study can be a positive step towards improving global LAI mapping.
机译:现有的全球叶面积指数(LAI)产品具有太大的针叶树季节变化的常见问题。由于许多混淆因素,远程感测赖的季节变化往往扭曲。我们提出了一种基于针寿命和气象条件来量化赖季度模式的方法。在本文中,我们在加拿大安大略省的两个针叶树架中测试了这种方法。针长生长和衰老率从针伸长率测量和落叶收集量量量化。通过测量平均针寿命并假设莱的动态平衡为全年周期,每年更新的针数量可以估计。然后,基于这两个利率,这是季节性的“分布式”,以便估计新和旧针队的季节性变化。将这些测量的Lai季节性轨迹与TRAC(追踪辐射和檐篷结构)和赖2000植物冠层分析仪进行了比较。从针生长和跌落的峰值与光学仪器之间的差异在15%内限制。同时,来自针头生长的Lai值与遥感数据相比,赖源于较大的季节,从遥感数据中呈赖索,导致针头的季节性变化比使用遥感数据从算法中检索到。我们相信这项研究可能是改善全球赖映射的积极步骤。

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