Monitoring canopy-scale autumn leaf phenology at fine-scale using unmanned aerial vehicle (UAV) photography
Monitoring canopy-scale autumn leaf phenology at fine-scale using unmanned aerial vehicle (UAV) photography
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DOI：
10.1016/j.agrformet.2023.109372
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发表时间：
2023-04
影响因子：
6.2
通讯作者：
Wenyan Ge;Xiuxia Li;L. Jing;Jianqiao Han;Fei Wang
中科院分区：
文献类型：
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作者：
Wenyan Ge;Xiuxia Li;L. Jing;Jianqiao Han;Fei Wang
落叶林物候的研究对了解温带落叶林生态系统具有重要意义。最近，随着近地表传感器，特别是无人机（UAV）的发展，对植被物候的精细尺度透视已经进行。然而，无人机衍生指数的冠层尺度的物候监测的能力仍然研究不足。利用DJI Phantom 4多光谱无人机（P4-M）对黄土高原温带落叶林秋季冠层酚盐（落叶开始期（SOF）、落叶中期（MOF）和落叶结束期（MOF））的监测能力进行了评价。使用一年内获得的104个无人机采集数据，探索了用于树冠尺度物候期估计的无人机衍生植被指数（归一化差异植被指数，NDVI）和两个颜色通道指数（绿色和红色色度坐标，GCC和RCC）。结果发现，NDVI表现出最好的匹配与视觉解释在冠层秋季表现型捕获（相关系数（R）= 0.69的植被指数）。NDVI与GCC具有较高的一致性（R ≥ 0.51，均方根误差≤ 10.44天，|偏置|≤ 6.71 d），除万年青乔木的多器官功能衰竭（MOF）和退化（Escherichia coli）外，其余均优于RCC。RCC获得最早的SOF，由于其对红叶的敏感性，但未能跟踪冠层物候，特别是在山区，因为它无法消除光照的影响。一般来说，NDVI表现出最高的潜力，估计秋季树冠尺度物候，其次是GCC和RCC。此外，地形通过控制植被类型和组成间接影响植被物候期。这项研究的结果表明，相当大的潜力无人机衍生的指数，特别是NDVI和GCC，秋季冠层物候监测在一个精细的尺度。
Investigation of deciduous forest phenology is significant for understanding temperate deciduous forest ecosystems. Fine-scale perspectives on vegetation phenology have recently been carried out with the development of near-surface sensors, particularly uncrewed aerial vehicles (UAV). However, the capability of UAV-derived indices for canopy-scale phenology monitoring remains under-studied. In this study, a DJI Phantom 4 Multispectral (P4-M) UAV was used to evaluate the abilities of drone-derived indices for autumn canopy phenodates (the start (SOF), middle (MOF) and end (EOF) of leaf fall) monitoring in a temperate deciduous forest on the Loess Plateau. Using 104 drone acquisitions acquired in one year, a UAV-derived vegetation index (normalized difference vegetation index, NDVI) and two color-channel indices (green and red chromatic coordinate, GCC and RCC) were explored for tree-crown scale phenophases estimation. It was found that NDVI exhibited the best matches with visual interpretation in canopy autumn phenodate capture (correlation coefficient (R) = 0.69 for EOF). NDVI and GCC showed a higher consistency (R ≥ 0.51, root-mean-square-error ≤ 10.44 days and |bias| ≤ 6.71 days) in extracting the phenometrics than RCC, except for the MOF and EOF of evergreen trees. RCC obtained the earliest SOF due to its sensitivity to red leaves but failed to track canopy phenology, especially EOF, in mountainous areas because of its inability to eliminate illumination effects. Generally, NDVI showed the highest potential for estimating autumn tree-crown scale phenometrics, followed by GCC and RCC. Moreover, topography indirectly impacts vegetation phenophases by controlling vegetation type and composition. The results of this study indicate the considerable potential of UAV-derived indices, particularly NDVI and GCC, for autumn canopy phenology monitoring at a fine scale.