Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle

Gabriel Araújo E Silva Ferraz,Leonardo Conti,Giuseppe Rossi,Lucas Santos Santana,Patrícia Ferreira Ponciano Ferraz,Diego Bedin Marin,Brenon Dienevam Souza Barbosa,Luana Mendes Dos Santos,Stefano Camiciottoli

doi:10.1080/22797254.2020.1845104

Gabriel Araújo E Silva Ferraz, Leonardo Conti + Show 7 more

Open Access

https://doi.org/10.1080/22797254.2020.1845104

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Abstract

ABSTRACT This study aimed to explore the influence of flight altitude, density, and distribution of ground control points (GCPs) on the digital terrain model (DTM) in surveys conducted by unmanned aerial vehicles (UAVs). A total of 144 photogrammetric projects consisting of 399 aerial photos were carried out in a 2 ha area. These photogrammetric projects involved six GCP distributions (edge, center, diagonal, parallel, stratified, and random), six GCP densities, and four flight altitudes (30, 60, 90, and 120 m). The response surface methodology was used to find interference factors and total root-mean-square error (RMSEt) as well. The 60 m flight altitude presented was the most efficient. Central GCP distribution was observed to have low precision. Using stratified and random edge distributions, 10 GCPs are recommended to achieve geometric precision below 0.07 m at any flight height. However, for studies requiring up to 0.07 m precision, the best distribution was parallel with 4 GCPs at any altitude. Diagonal positioning of the GCPs showed RMSEt values below 0.11 m with 4 GCPs at any altitude. A good distribution of GCPs was found to be important, but the density of GCPs per image was more relevant when obtaining a lower RMSEt.

Highlights

Precision agriculture is a useful model in the management of natural resources and improvement of modern agriculture. (Orozco & Llano Ramírez, 2016; Far & RezaeiMoghaddam, 2018)
This study aimed to explore the influence of flight altitude, density, and distribution of ground control points (GCPs) on the digital terrain model (DTM) in surveys conducted by unmanned aerial vehicles (UAVs)
Images collected, the better the spatial resolution results. This phenomenon, which was observed by Mesas-Carrascosa, García, De Larriva et al (2016) revealed that spatial ee resolution was directly related to flight altitude and could be predefined to achieve rR

Summary

Introduction

Precision agriculture is a useful model in the management of natural resources and improvement of modern agriculture. (Orozco & Llano Ramírez, 2016; Far & RezaeiMoghaddam, 2018). Precision agriculture is a useful model in the management of natural resources and improvement of modern agriculture. Aerial remote sensing presents new methods of research and work optimization, capturing terrestrial features using unmanned aerial vehicles (UAVs). Some applications of UAVs in agriculture were presented in studies of variables related to nitrogen in corn (Corti et al, 2019), the. Image collection using UAVs and their photogrammetric applications offers the ee possibility to observe agricultural fields from a different point of view. It is possible to observe some field aspects that are relatively invisible when monitored from the ground (Candiago, Remondino, De Giglio et al, 2015; Polo, Hornero, Duijneveld et ev al., 2015; Rodríguez-Fernández, Menéndez & Camacho et al, 2017). UAVs iew offer other advantages, such as flexibility in collecting images, improved spatial resolution, and control over temporal resolution

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