Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

Abstract

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 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.

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging

“…However, the flight time at 70 m is significantly less than the flight time at the other two altitudes, with only 17.56 minutes compared to 45.35 minutes at 35 m and 42.47 minutes at 45 m ( Table 1 ). Similar results were reported by Avtar et al [ 56 ], who found that (1) the CA derived at flight altitudes of 20 m, 60 m, and 80 m yields a similar correlation with the ground truth of young oil palm ( Elaeis guineensis ); (2) tree heights derived from 60 m and 80 m flights showed slightly higher correlations with ground truth data than those derived from the 20 m flight altitude. Moreover, Sadeghi and Sohrabi [ 57 ] found that a higher flight altitude (80-140 m) yielded more accurate height measurements than a lower flight altitude.…”

UAV-Based Disease Detection in Palm Groves of Phoenix canariensis Using Machine Learning and Multispectral Imagery

“…Using high-resolution (3.94 cm) UAV multispectral imagery and ML algorithms, a probabilistic classification model was built to detect potential infections in Phoenix canariensis. The spatial resolution was achieved with 60 m altitude drone flights, an optimal flight altitude that has been proven to enhance the biophysical parameters extraction of vegetation [67]. This approach represents the first attempt to identify and detect possible infections on a palm tree scale in the Canary Islands based on spectral response.…”

Digital Terrain Modelling by Remotely Piloted Aircraft: Optimization and Geometric Uncertainties in Precision Coffee Growing Projects

“…The 90 × 90 overlaps added a high level of detail to the DEMs, and when compared with traditional topographic methods, these overlap levels were considered inferior. High levels of detail combined with an altitude reduction were reported by Avtar et al [50], who evaluated different flight heights for biophysical analysis of palm trees, and the authors observed a significant contribution of lower altitudes to an increase in errors. This result was attributed to the high level of detail in the image.…”