The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced using multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area.

中文翻译：

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评估无人机高度对年轻油棕提取生物物理参数的影响

有关生物物理参数的信息（例如高度，树冠面积和植被指数，例如归一化差异植被指数（NDVI）和归一化差异红边指数（NDRE）），对于监测健康状况和油棕树的生长非常有用。精准农业实践。安装在无人机（UAV）上的多光谱传感器的使用为研究植物健康提供了高时空分辨率数据。然而，从多光谱传感器提取油棕的生物物理参数时，无人机高度的影响尚未得到很好的探索。因此，本研究利用安装在DJI Phantom-4无人机平台上的MicaSense RedEdge传感器进行航空摄影测量。在飞行高度为20 m，60 m，马来西亚年轻的油棕种植区上方的地面高度（AGL）为80 m。使用Pix4DMapper软件中的运动结构（SfM）技术对图像进行处理，并使用多光谱正射影像，数字表面模型（DSM）和点云生成图像。同时，通过减去DSM和数字高程模型（DEM）生成冠层高度模型（CHM）。从CHM和正马赛克中提取了油棕树的高度和树冠投影面积（CPA）。NDVI和NDRE是使用正镶嵌数据的红色，红色边缘和近红外光谱带计算的。提取的高度和CPA的准确性是通过评估来自地面测量的CPA和高度的不同高度的无人机数据的准确性来评估的。相关性，均方根偏差（RMSD），利用中心趋势和趋势将无人机提取的生物物理参数与地面数据进行比较。根据我们的结果，在60 m的高度飞行是估算生物物理参数的最佳和最佳飞行高度，其次是80 m高度。20 m UAV高度显示出年轻油棕的生物物理参数被高估的趋势，而在提取其他参数时不一致。该方法和结果是在油棕种植区迈向精确农业的一步。20 m UAV高度显示出年轻油棕的生物物理参数被高估的趋势，而在提取其他参数时不一致。该方法和结果是在油棕种植区迈向精确农业的一步。20 m UAV高度显示出年轻油棕的生物物理参数被高估的趋势，而在提取其他参数时不一致。该方法和结果是在油棕种植区迈向精确农业的一步。