![]() The author would like to thank Roodkhiz Water and Environment Company for collecting the UAV images.To explore the contribution of UAVs to the spatial mapping process in urban environments, a team from Greece studied the use of aerial imagery to achieve wide coverage of a predefined geometrical area of interest. Comparing RGB-based vegetation indices from UAV imageries to estimate hops canopy area. Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Comparing RGB-based vegetation indices with NDVI for drone-based agricultural sensing. As a result, UAV-based RGB images will be an invaluable source of data for green area management in urban and rural areas. In addition to opening up a new era of applications, the RGB-derived vegetation indices can be calibrated and validated more accurately using multispectral UAV images. Multispectral UAV images can be used for many applications such as urban tree mapping, horticulture, precision agriculture and more. R: Red, G: Green, B: Blue, NIR: Near-infrared, and RE: Red-edge.Ĭonclusion - Value of Multispectral UAV Images As a result, shadow areas are highlighted as vegetation. The CIVE, VDVI, ExG and ExR indices are sensitive to shadows. The NDI and VEG indices provided similar results and outperformed other visible-band indices. Additionally, buildings and non-vegetated areas are clearly highlighted by all indices. The corresponding vegetation index maps for the study area are shown in Figure 4.Īlthough trees and lawns are highlighted by all vegetation indices, vegetation areas are more distinguishable by NDVI. The well-known multispectral and visible-band vegetation indices such as NDVI, NDRE, NGRDI, VIDVI, CIVE, ExG, ExR and VEG were utilized. The multispectral orthomosaic derived from the photogrammetric processing of the UAV images was used to calculate vegetation indices as indicated in Table 1. Results of Multispectral Orthomosaic Derived from Photogrammetric Processing of UAV Images ![]() The 3D point cloud with a density of 900 points/m² and orthomosaic with a ground sampling distance (GSD) of 3 centimetres were generated from the point clouds and the images (Figure 3).įigure 3: True and colour-coded dense point cloud of the study area. The processing workflow – including image alignment to produce sparse point clouds, build dense cloud, build mesh, build texture, build the digital elevation model (DEM) and build the orthomosaic – was performed and lastly, to generate a 3D map of the study area, the multispectral point clouds and orthomosaic were exported in (.las) and (.tiff) formats, respectively. The photogrammetric processing of the UAV images was carried out using Agisoft Metashape software. This enables the most accurate NDVI results to be achieved.įigure 2: BNUT campus (orange line) and study area (green line). More importantly, an integrated spectral sunlight sensor on top of the UAV captures solar irradiance to maximize the accuracy and consistency of data collection at different times of the day. All cameras benefit from the calibration process whereby radial and tangential lens distortions are measured and saved into each image’s metadata to ease post-processing of the images. This fixes the positioning data to the centre of the CMOS and ensures that each image uses the most accurate metadata. Real-time, centimetre-accurate positioning data on images captured by all six cameras within DJI’s built-in system is used to align the flight controller, RGB/multispectral cameras and RTK module. In the DJI P4 multispectral, images are collected by an RGB camera and a multispectral camera array with five global shutter cameras covering blue, green, red, red-edge, and near-infrared bands at a resolution of 1,600 x 1,300 pixels (Figure 1). Therefore, imagery data collection for vegetation mapping is now simpler and more efficient than ever before. DJI recently introduced the P4 multispectral, a high-precision unmanned aerial vehicle (UAV or ‘drone’) which exploits the integration of multispectral cameras to facilitate agricultural and environmental monitoring applications.
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