Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands
Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands
复制标题
DOI：
10.3390/rs14143453
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
Emma C. Hall;M. Lara
中科院分区：
文献类型：
--
作者：
Emma C. Hall;M. Lara
无人驾驶航空系统（UAS）已成为强大的生态观测平台，能够填补关键的空间和光谱观测空白的植物生理和物候特征，一直难以测量从星载传感器。尽管最近技术取得了进步，但无人机传感器的高成本限制了无人机系统技术在科学学科中的广泛应用。在这里，我们评估了现成的和复杂的无人机携带的传感器映射植物物种和植物功能类型（PFT）在不同的草原之间的权衡。具体来说，我们比较了物种和PFT映射精度来自高光谱，多光谱和RGB图像融合光探测和测距（LiDAR）或结构运动（SfM）衍生的冠层高度模型（CHM）。传感器数据融合用于考虑单个观测期或近月观测频率，以整合物候信息（即，phenometrics）。结果表明，植物物种和PFT的整体分类准确率在高光谱和LiDAR-CHM融合中最高（分别为78%和89%），其次是多光谱和表型-SfM-CHM融合（分别为52%和60%）以及RGB和SfM-CHM融合（分别为45%和47%）。我们的研究结果表明，从经济与过高的传感器网络的映射精度的明确权衡，但强调，现成的多光谱传感器可以实现的精度与复杂的UAS传感器集成到机器学习图像分类器的phenometrics。
Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers.