基于低空无人机遥感的冬小麦覆盖度变化监测

无人机遥感作为卫星遥感的有益补充,具有高时效、高分辨率、低成本、低损耗、低风险及可重复等优点。为了利用无人机遥感系统进行快速机动地监测大面积农作物覆盖度变化,更好地服务和指导农业生产,该文设计了一套以低空无人直升机为平台的多光谱载荷观测系统,并以冬小麦为研究对象,对冬小麦生长过程中的5个主要生育期进行监测,提出一种从时间序列影像的植被指数直方图曲线中获取植被指数阈值的方法,并利用植被指数阈值法提取研究区域内冬小麦覆盖度时序变化曲线,分析了空间尺度对提取植被覆盖度的影响。研究结果表明,利用低空无人机遥感监测冬小麦覆盖度变化的方法可行,分析结果可靠,在大面积农作物覆盖度的测量有很好的应用前景。

Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system

As a complement to satellite observation, the technique of Unmanned Aerial Vehicle (UAV) shows great advantages in monitoring crop stages at a large scale because of its high spatiotemporal resolutions, low cost and risk in field measurements. With the objective to validate the availability of UAV in reality, this paper designed and established a low-altitude land surface UAV observation system consisted of a UAV platform and a multispectral camera to obtain images of winter wheat at its five growth stages. On basis of these images, this paper proposed a new approach that using time-series histograms of vegetation index to refine the threshold value in the VI-threshold method for extracting vegetated pixels from images. The results show that the UAV system is available to obtain surface images feasibly and the new vegetated pixel retrieval method can provide reasonable fractional vegetation cover (FVC) which is generally consistent with the five growth stages of the winter wheat. Moreover, this paper also investigated the spatial scaling effects of estimate FVC using the images from the UAV system.