Vegetation monitoring using multispectral sensors - best practices and lessons learned from high latitudes
Vegetation monitoring using multispectral sensors - best practices and lessons learned from high latitudes
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DOI：
10.1139/juvs-2018-0018
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发表时间：
2019-03-01
影响因子：
2.3
通讯作者：
Myers-Smith, Isla H.
中科院分区：
文献类型：
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作者：
Assmann, Jakob J.;Kerby, Jeffrey T.;Myers-Smith, Isla H.
快速的技术进步极大地提高了无人机(UAV)及其相关传感器的可负担性和可及性。紧凑型多光谱无人机传感器在电磁光谱的可见光和近红外部分捕获高分辨率图像，从而能够计算植被指数，例如用于生产力估计和植被分类的归一化差异植被指数(NDVI)。尽管技术取得了进步，但在获取高质量数据方面仍然存在挑战，这凸显了标准化工作流程的必要性。在这里，我们讨论了使用多光谱无人机传感器进行植被监测的挑战、技术方面和实际考虑，并以鹦鹉红杉(巴黎，法国)传感器为例，提出了基于遥感原理和我们在高纬度环境中的现场经验的工作流程。我们专注于与太阳角度、天气条件、地理位置和辐射校准相关的关键误差源，并估计它们的相对贡献，这些误差源可能导致我们冻土带野外地点旺季NDVI估计的+/-10%以上的不确定度。我们的发现表明，这些错误可以归因于改进的飞行计划、元数据收集、地面控制点部署、反射目标的使用和质量控制。有了标准化的最佳实践，多光谱传感器可以提供有意义的空间数据，这些数据在空间和时间上是可重现和可比较的。
Rapid technological advances have dramatically increased affordability and accessibility of unmanned aerial vehicles (UAVs) and associated sensors. Compact multispectral drone sensors capture high-resolution imagery in visible and near-infrared parts of the electromagnetic spectrum, allowing for the calculation of vegetation indices, such as the normalised difference vegetation index (NDVI) for productivity estimates and vegetation classification. Despite the technological advances, challenges remain in capturing high-quality data, highlighting the need for standardized workflows. Here, we discuss challenges, technical aspects, and practical considerations of vegetation monitoring using multispectral drone sensors and propose a workflow based on remote sensing principles and our field experience in high-latitude environments, using the Parrot Sequoia (Pairs, France) sensor as an example. We focus on the key error sources associated with solar angle, weather conditions, geolocation, and radiometric calibration and estimate their relative contributions that can lead to uncertainty of more than +/- 10% in peak season NDVI estimates of our tundra field site. Our findings show that these errors can be accounted for by improved flight planning, metadata collection, ground control point deployment, use of reflectance targets, and quality control. With standardized best practice, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.