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Commercial Off-the-Shelf Digital Cameras on Unmanned Aerial Vehicles for Multitemporal Monitoring of Vegetation Reflectance and NDVI

机译:无人驾驶飞机上的商用现成数码相机,用于植被反射率和NDVI的多时相监测

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This paper demonstrates the ability to generate quantitative remote sensing products by means of an unmanned aerial vehicle (UAV) equipped with one unaltered and one near infrared-modified commercial off-the-shelf (COTS) camera. Radiometrically calibrated orthomosaics were generated for 17 dates, from which digital numbers were corrected to surface reflectance and to normalized difference vegetation index (NDVI). Validation against ground measurements showed that 84%-90% of the variation in the ground reflectance and 95%-96% of the variation in the ground NDVI could be explained by the UAV-retrieved reflectance and NDVI, respectively. Comparisons against Landsat 8 data showed relationships of for reflectance and for NDVI. It was not possible to generate a fully consistent time series of reflectance, due to variable illumination conditions during acquisition on some dates. However, the calculation of NDVI resulted in a more stable UAV time series, which was consistent with a Landsat series of NDVI extracted over a deciduous and evergreen woodland. The results confirm that COTS cameras, following calibration, can yield accurate reflectance estimates (under stable within-flight illumination conditions), and that consistent NDVI time series can be acquired in very variable illumination conditions. Such methods have significant potential in providing flexible, low-cost approaches to vegetation monitoring at fine spatial resolution and for user-controlled revisit periods.
机译:本文演示了通过配备一台未更改和一台近红​​外修改的商用现货(COTS)摄像机的无人机(UAV)来生成定量遥感产品的能力。经过辐射校准的正射镶嵌图生成了17个日期,从中将数字编号校正为表面反射率和归一化差异植被指数(NDVI)。针对地面测量的验证表明,地面反射率变化的84%-90%和地面NDVI变化的95%-96%可以分别由无人机获取的反射率和NDVI来解释。与Landsat 8数据的比较显示了反射率和NDVI的关系。由于某些日期的采集过程中照明条件变化,因此无法生成完全一致的反射时间序列。但是,NDVI的计算导致了更稳定的无人机时间序列,这与从落叶和常绿的林地上提取的NDVI的Landsat系列一致。结果证实,经过校准后,COTS摄像机可以得出准确的反射率估计值(在稳定的机内照明条件下),并且可以在变化很大的照明条件下获得一致的NDVI时间序列。这样的方法在以精细的空间分辨率和用户控制的重新访问时段提供灵活,低成本的植被监测方法方面具有巨大潜力。

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