Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations
Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations
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DOI：
10.1016/j.rse.2019.01.010
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发表时间：
2019-03-15
影响因子：
13.5
通讯作者：
Barr, Stuart
中科院分区：
文献类型：
--
作者：
Berra, Elias Fernando;Gaulton, Rachel;Barr, Stuart
以具有成本效益的方式，在精细的空间尺度和相对较大的地区监测森林物候仍然是一项重大挑战。为了解决这一问题，无人驾驶飞行器（uav）似乎是森林物候监测的潜在新平台。本文评估了无人机数据在跟踪春季物候动态方面的潜力，从单个树木到林地尺度，并将无人机结果与地面和卫星观测进行交叉比较，以便更好地了解无人机数据的特征，并评估在卫星衍生物候验证中使用的潜力。2015年春季，在15公顷的混合林地上进行了密集的地面行动，同时获得了无人机时间序列数据（5厘米空间分辨率，类似于7天时间分辨率）。利用归一化植被指数（NDVI）和绿色色度坐标（GCC）的无人机时间序列估算了单个树水平的物候过渡日期，并与树木物候的目视观测结果进行了验证。无人机衍生的季节开始日期可以以< 1周的精度预测。分析被扩展到地块水平，其中地面（视觉评估和下层发展）、无人机和Landsat指标进行了比较，表明无人机数据对于跟踪冠层物候是有效的，而不是由卫星检测的生态系统动态。无人机数据被用于自动绘制整个林地中单个树木的物候事件，表明在单个Landsat像素的范围内可以发生对比的冠层物候事件。这一点，再加上Landsat系列中较大的时间差距，解释了本研究中无人机和Landsat衍生的表型之间的差关系（R-2 < 0.50）。现在有机会在连续的植被群落上跟踪非常精细的地表变化，提供可以在多个尺度上改进植被音系特征的信息。
The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. This article assesses the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data (5 cm spatial resolution, similar to 7 day temporal resolution) were acquired in tandem with an intensive ground campaign during the spring season of 2015 across a 15 ha mixed woodland. Phenophase transition dates were estimated at an individual tree-level using UAV time series of Normalized Difference Vegetation Index (NDVI) and Green Chromatic Coordinate (GCC) and validated against visual observations of tree phenology. UAV-derived start of season dates could be predicted with an accuracy of < 1 week. The analysis was scaled to a plot level, where ground (visual assessment and understorey development), UAV and Landsat metrics were compared, indicating UAV data is effective for tracking canopy phenology, as opposed to ecosystem dynamics detected by satellites. The UAV data were used to automatically map phonological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This, and a large temporal gap in the Landsat series, accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R-2 < 0.50) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, providing information which could improve characterization of vegetation phonology at multiple scales.