Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
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DOI：
10.3390/drones4030056
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发表时间：
2020-09
期刊：
影响因子：
4.8
通讯作者：
J. Atkins;A. Stovall;Xi Yang
中科院分区：
文献类型：
--
作者：
J. Atkins;A. Stovall;Xi Yang
物候学是气候变化对生态系统影响的一个明显标志。因此，监测植被物候的时空格局对了解地球系统的变化具有重要意义。各种各样的传感器被用于监测植被物候，包括安装在各种平台上的具有不同观察几何形状的数码相机。传感器视角、视角和分辨率可能会影响物候的估计。我们比较了三种不同的植被物候遥感方法——一种基于无人飞行器（UAV）的、朝下的RGB相机，一种基于树冠下、朝上的带有蓝色(B)、绿色(G)和近红外（NIR）波段的半球形相机，以及一种基于塔式RGB PhenoCam的、定位于树冠斜角的方法，以估计美国弗吉尼亚州中部温带混合物种森林的春季物候向冠层关闭的转变。本研究的两个目标是：(1)通过将冠层下半球形照片与高空间分辨率（0.03 m）无人机影像进行匹配，比较冠层绿度（利用绿色坐标和归一化植被指数）和冠层结构属性（叶面积和间隙分数）的冠层上下推断，找到合适的空间覆盖和分辨率进行比较；(2)比较无人机、地面和塔上图像在估计春季物候转变时间方面的表现。我们发现，在该系统中，无人机图像的空间缓冲半径为20 m与冠层下图像的可比性最接近。从春季物候期到生长季节，当冠层绿度稳定时，传感器和平台在+/ - 5天内一致。我们表明，将无人机图像与基于塔的观测平台和基于样地的物候研究（例如，长期监测、现有研究网络和永久样地）相结合，有可能通过无人机图像扩展基于样地的森林结构测量，约束物候期的不确定性估计，并更可靠地评估站点异质性。
Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.