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.
Highlights
The oil palm (Elaeis guineensis) is an important industrial cash crop for major producer countries such as Indonesia, Malaysia, and Thailand, which provide sizeable economic benefits both from employment and income through exports [1]
This research was undertaken to evaluate the influence of unmanned aerial vehicles (UAV) flight altitude on the extraction of biophysical parameters of oil palm plantations
canopy height models (CHM) was generated by subtraction of digital surface model (DSM) with digital elevation models (DEM)
Summary
The oil palm (Elaeis guineensis) is an important industrial cash crop for major producer countries such as Indonesia, Malaysia, and Thailand, which provide sizeable economic benefits both from employment and income through exports [1]. The health of oil palm can be monitored by studying the biophysical parameters such as height, crown size, and vegetation vigor. In the oil palm industry, UAV-based imaging provides low cost flexible data acquisition with less weather constraints and higher spatial/temporal resolution, as compared to high-resolution satellite data [15]. There is a lack of studies about the influence of low flight altitude on the extraction of biophysical parameters of young oil palm. It is necessary to understand the influence of flight altitude on derived biophysical parameters of young oil palm for precision agriculture studies. This study attempts to compare the influence of different flight altitudes to derive biophysical parameters of young oil palm using the SfM technique
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