Estimation of plant area index and phenological transition dates from digital repeat photography and radiometric approaches in a hardwood forest in the Northeastern United States
Estimation of plant area index and phenological transition dates from digital repeat photography and radiometric approaches in a hardwood forest in the Northeastern United States
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DOI：
10.1016/j.agrformet.2017.09.004
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发表时间：
2018-02
影响因子：
6.2
通讯作者：
Motomu Toda;A. Richardson
中科院分区：
文献类型：
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作者：
Motomu Toda;A. Richardson
长期，连续的数码相机图像和塔式辐射监测进行了在美国东北部的一个代表性的硬木森林网站，AmeriFlux网络的一部分。在这项研究中，叶面积指数（LAI）、植物面积指数（PAI）和相关过渡日期（例如，叶扩展的开始和叶脱落的停止的时间）使用来自Bartlett实验森林的4年数据进行比较。我们使用了数字重复摄影（DRP）的图像收集使用两种不同的方法（“冠层覆盖”和“phenocam”的方法），连同上面和下面的冠层光合有效辐射（PAR）的测量。从冠层覆盖图像（LAICANOPY）和冠层上方和下方PAR测量（LAIfPARt）估计的生长期LAI在幅度方面与先前多种比较方法的结果大致相同，尽管生长期LAICANOPY略低于LAIfPARt（3.19 m2 m − 2至3.67 m2 m − 2）。此外，我们从PAICANOPY，PAIfPARt和基于颜色的指标计算的phenocam图像（绿色（GCC）和红色（RCC）色坐标）的物候过渡日期。在春季和秋季的过渡日期有所不同，根据方法，大概是由于每个植被度量的植被状态检测能力。我们发现，叶面积指数估计冠层覆盖图像可能会受到自动曝光设置，这限制了在春季和秋季的过渡阶段，在物候检测微妙的变化的能力。特别是在秋季，从phenocam图像计算的基于颜色的度量与叶面积动态解耦，从而PAI。虽然以上和以下冠层PAR测量可以产生更好的指标，估计叶面积指数，其季节动态，并在长期监测相关的物候过渡日期，我们认为，有明显的好处，这里使用的多传感器的方法。
Long-term, continuous digital camera imagery and tower-based radiometric monitoring were conducted at a representative hardwood forest site in the Northeastern United States, part of the AmeriFlux network. In this study, the phenological metrics of the leaf area index (LAI), plant area index (PAI) and associated transition dates (e.g., timing of the onset of leaf expansion and the cessation of leaf fall) were compared using 4-year of data from Bartlett Experimental Forest. We used digital repeat photography (DRP) imagery collected using two different methods (“canopy cover” and “phenocam” approaches), together with above- and below-canopy measurements of photosynthetically active radiation (PAR). The growth-period LAI estimated from canopy cover images (LAICANOPY) and the above and below canopy PAR measurements (LAIfPARt) were within approximately the same range, in term of magnitude, as previous results for multiple comparative methods, although growing-season LAICANOPYwas slightly lower (3.11 m2m−2to 3.35 m2m−2) than LAIfPARt(3.19 m2m−2to 3.67 m2m−2). In addition, we derived phenological transition dates from PAICANOPY, PAIfPARt, and color-based metrics calculated from the phenocam imagery (green (GCC) and red (RCC) chromatic coordinates). The transition dates in both spring and autumn differed somewhat according to method, presumably due to the vegetation status detection abilities of each vegetation metric. We found that LAI estimation from canopy cover images may be influenced by automatic exposure settings, which limits the ability to detect subtle changes in phenology during the transition phases in both spring and autumn. Particularly in autumn, the color-based metrics calculated from the phenocam imagery are decoupled from leaf area dynamics and thus PAI. While above and below canopy PAR measurements could yield the better indicators for estimating LAI, its seasonal dynamics, and associated phenological transition dates in long-term monitoring, we argue that there are obvious benefits to the multi-sensor approach used here.