Statistical process control and mapping accuracy standards applied to aerial surveys

Vinícius Bitencourt Campos Calou, Adunias dos Santos Teixeira, José Adriano da Silva, Márcio Regys Rabelo de Oliveira, Ícaro Vasconcelos do Nascimento

Resumo


Remotely piloted aircraft (RPA) are established in the market as a powerful tool for acquiring aerial images and facilitating mapping for various purposes. The aim of this study was to evaluate the quality of processes originating from the generation of georeferenced digital products employing a differing number of ground control points (GCP), using Statistical Process Control (SPC) and Mapping Accuracy Standards (MAS) in an orthomosaic produced with images from an RPA. A multirotor RPA was used to acquire aerial images over an area of 2 hectares. An orthomosaic was later generated using the PhotoScan software, and georeferenced with eight, five and three GCP (ground control points). Positioning errors were submitted to SPC to evaluate the quality of each process, and the orthomosaics were qualified by MAS. The results are promising, in view of the positioning errors of less than 0.1 m in the generated orthomosaics, which are classified as Mapping Accuracy Standards class ‘A’. Statistical Process Control showed acceptable levels of error, indicating the high accuracy of surveys of this nature. The precision obtained when mapping shows that aerial images obtained by means of RPA can be used in topographic surveys as long as error standards and process control are observed, attesting to the quality of the results.

Palavras-chave


Topography; PhotoScan; Drone; Structure from motion; RPA

Texto completo:

PDF

Referências


AGÜERA-VEGA, F.; CARVAJAL-RAMÍREZ, F.; MARTÍNEZ-CARRICONDO, P. Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle. Measurement, v. 98, p. 221-227, 2017.

ALSALAM, B. H. Y. et al. Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture. In: IEEE AEROSPACE CONFERENCE, 38., 2017, Big Sky. Proceedings […]. Big Sky: IEEE, 2017. p. 1-12.

ANUROGO, W. et al. A simple aerial photogrammetric mapping system overview and image acquisition using unmanned aerial vehicles (UAVs). Geospatial Information, v. 1, n. 1, p. 11-18, 2017.

BACHMANN, F. et al. Micro UAV based georeferenced orthophoto generation in vis+ nir for precision agriculture. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 1, p. W2, 2013.

CALOU, V. B. C. et al. Estimation of maize biomass using Unmanned Aerial Vehicles. Engenharia Agrícola, v. 39, n. 6, p. 744-752, 2019.

FLYNN, K. F.; CHAPRA, S. C. Remote sensing of submerged aquatic vegetation in a shallow non-turbid river using an unmanned aerial vehicle. Remote Sensing, v. 6, n. 12, p. 12815-12836, 2014.

FUNDAÇÃO CEARENSE DE METEOROLOGIA E RECURSOS HÍDRICOS. Dados de pluviometria por faixa de anos - Estado do Ceará. 2018.

GÓMEZ-CANDÓN, D.; DE CASTRO, A. I.; LÓPEZ-GRANADOS, F. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, v. 15, n. 1, p. 44-56, 2014.

HUGENHOLTZ, C. H. et al. Geomorphological mapping with a small unmanned aircraft system (sUAS): feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model. Geomorphology, v. 194, p. 16-24, 2013.

LIMA, R. P. et al. The use of RPAS-Remotely Piloted Aircraft Systems in the topographic mapping for mining. REM-International Engineering Journal, v. 71, n. 2, p. 281-287, 2018.

MICELI, B. S. et al. Avaliação vertical de modelos digitais de elevação (MDEs) em diferentes configurações topográficas para médias e pequenas escalas. Revista Brasileira de Cartografia, v. 63, n. 1, 2011.

MONTGOMERY, D. C. Introdução ao controle estatístico da qualidade. 4. ed. São Paulo: LTC, 2004. 513 p.

NORONHA, R. H. de F. et al. Controle estatístico aplicado ao processo de colheita mecanizada diurna e noturna de cana-de-açúcar. Bragantia, v. 70, n. 4, p. 931-938, 2011.

SALVINI, R. et al. Use of a remotely piloted aircraft system for hazard assessment in a rocky mining area (Lucca, Italy). Natural Hazards & Earth System Sciences, v. 18, n. 1, 2018.

SIEBERT, S.; TEIZER, J. Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Automation in Construction, v. 41, n. 1, p. 1-14, 2014.

SILVA, R. P. et al. Qualidade da colheita mecanizada de feijão (Phaseolus vulgaris) em dois sistemas de preparo do solo. Revista Ciência Agronômica, v. 44, n. 1, p. 61-69, mar. 2013.

TAKAHASHI, F. H. et al. Variação e monitoramento da qualidade do leite através do Controle Estatístico de Processos. Ciência Animal Brasileira, v. 13, n. 1, 2012.

TSOUROS, D. C.; BIBI, S.; SARIGIANNIDIS, P. G. A review on UAV-based applications for precision agriculture. Information, v. 10, n. 11, p. 349, 2019.

ZANETTI, J.; GRIPP JUNIOR, J.; SANTOS, A. de P. dos. Influence of number and distribution of control points on orthophotos generated from a survey by VANT. Revista Brasileira de Cartografia, v. 69, n. 2, p. 263-277, 2017.

ZARCO-TEJADA, P. J. et al. Tree height quantification using very high-resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, v. 55, n. 1, p. 89-99, 2014.




Revista Ciência Agronômica ISSN 1806-6690 (online) 0045-6888 (impresso), Site: www.ccarevista.ufc.br, e-mail: ccarev@ufc.br - Fone: (85) 3366.9702 - Expediente: 2ª a 6ª feira - de 7 às 17h.