ABSTRACT Unmanned aerial vehicles (UAVs) have the potential to provide highly detailed information on vegetation status useful in precision agriculture. However, challenges are associated with existing techniques for UAV-based retrieval of vegetation biophysical variables such as leaf area index (LAI), including variable illumination, bidirectional reflectance effects, and the need for image calibration, mosaicking, and normalization. We investigated an alternative approach that avoids these challenges whilst still providing spatially explicit estimates of LAI, using UAV-based digital hemispherical photography (DHP). LAI estimates were obtained using a low-cost UAV-based DHP system over a winter wheat field in Southern England. Point-based estimates were interpolated to provide spatially continuous datasets, which successfully described patterns of vegetation condition. The UAV-based DHP data were compared to ground-based LAI estimates, demonstrating good agreement (root mean square error (RMSE) = 0.10, normalized RMSE (NRMSE) = 3%).

中文翻译：

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基于低成本无人机的数字半球摄影估算叶面积指数：可行性评估

摘要 无人驾驶飞行器 (UAV) 有可能提供非常详细的植被状况信息，可用于精准农业。然而，挑战与现有的基于无人机的植被生物物理变量检索技术有关，例如叶面积指数 (LAI)，包括可变照明、双向反射效应以及对图像校准、镶嵌和归一化的需求。我们研究了一种替代方法，该方法避免了这些挑战，同时仍然使用基于无人机的数字半球摄影 (DHP) 提供 LAI 的空间明确估计。LAI 估计值是使用低成本基于无人机的 DHP 系统在英格兰南部的冬小麦田上获得的。内插基于点的估计以提供空间连续的数据集，它成功地描述了植被状况的模式。将基于 UAV 的 DHP 数据与基于地面的 LAI 估计值进行比较，显示出良好的一致性（均方根误差 (RMSE) = 0.10，归一化 RMSE (NRMSE) = 3%）。