12/2/2023 0 Comments Uav drone mapping![]() ![]() 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. It does not store any personal data.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. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". ![]() The cookie is used to store the user consent for the cookies in the category "Other. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. ![]()
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