Skip to main content

Advertisement

Log in

Assessing the effect of flight altitude and overlap on orthoimage generation for UAV estimates of coastal wetlands

  • Published:
Journal of Coastal Conservation Aims and scope Submit manuscript

Abstract

There has been a constant increase in technology regarding unmanned aerial vehicle (UAV) systems in the last decade; yet the possibility of surveying large areas with such aerial platforms has been hampered by their low endurance. This situation has been exacerbated in low institutional capacity countries with high biodiversity coastal environments where constant monitoring is required for conservation purposes. The objective of this study was hence to determine an optimal flight mission over an initial area of 40 ha of coastal wetlands using a low-cost UAV system. The UAV missions were planned at altitudes of 60, 80, 100, and 120 m. Several orthoimages were generated with different along/across track overlaps (in intervals of 10% from 40 to 90%) in order to determine the minimum flight duration and computational efficiency. Results indicated that flight altitude and across track overlap were the most relevant variables in terms of flight distance, flight duration, number of generated images, and computer processing time. The optimal flight mission was found to be at 120-m altitude with an along/across track overlap of 60/50%. Based on the aforementioned parameters, we were able to survey a larger area of 200 ha with minimum flight duration and computer processing time. Our study highlights the feasibility and limitations of using a consumer-grade UAV in order to assess coastal wetland areas rapidly when field-based surveys approaches are not possible.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Akar Ö (2017) Mapping land use with using rotation forest algorithm from UAV images. European J Remote Sens 50(1):269–279

    Article  Google Scholar 

  • Almeida LP, Almar R, Bergsma EWJ, Berthier E, Baptista P, Garel E, Dada OA, Alves B (2019) Deriving high spatial-resolution coastal topography from sub-meter satellite stereo imagery. Remote Sens 11:590

    Article  Google Scholar 

  • Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11(3):138–146

    Article  Google Scholar 

  • Chabot D, Bird DM (2015) Wildlife research and management methods in the 21st century: where do unmanned aircraft fit in? J Unmanned Veh Syst 3:137–155

    Article  Google Scholar 

  • Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J Photogramm Remote Sens 92:79–97

    Article  Google Scholar 

  • Coops NC, Goodbody TRH, Cao L (2019) Four steps to extend drone use in research. Nature 572:433–435

    Article  Google Scholar 

  • Dandois JP, Olano M, Ellis EC (2015) Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sens 7:13895–13920

    Article  Google Scholar 

  • Flores-Verdugo F, Blanco-Correa M, Ortiz-Pérez MA (2009) Caracterización del manglar (estructura y distribución) y otros humedales del predio CIP-FONATUR en el complejo de barreras litorales de Las Cabras-Teacapán (Sinaloa): Consideraciones ambientales para su manejo, conservación y aprovechamiento sustentable. Universidad Nacional Autónoma de México, Mexico, Instituto de Geografía

    Google Scholar 

  • Fraser BT, Congalton RG (2018) Issues in unmanned aerial systems (UAS) data collection of complex forest environments. Remote Sens 10:908

    Article  Google Scholar 

  • García E (1998) Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO). Climas (Clasificación de Koppen, modificada por García) Escala 1:1,000,000, Mexico

  • Guisado-Pintado E, Jackson DWT, Rogers D (2019) 3D mapping efficacy of a drone and terrestrial laser scanner over a temperate beach-dune zone. Geomorphol 328:157–172

    Article  Google Scholar 

  • Guo M, Li J, Sheng C, Xu J, Wu L (2017) A review of wetland remote sensing. Sensors 17:777

    Article  Google Scholar 

  • Hardin PJ, Jensen RR (2011) Small-scale unmanned aerial vehicles in environmental remote sensing: challenges and opportunities. GISci Remote Sens 48(1):99–111

    Article  Google Scholar 

  • INEGI (Instituto Nacional de Estadística y Geografía) (2009) Anuario estadístico del estado de Sinaloa. INEGI, Ciudad de México

    Google Scholar 

  • Jayson-Quashigah PN, Addo WA, Amisigo B, Wiafe G (2019) Assessment of short-term beach sediment change in the Volta Delta coast in Ghana using data from unmanned aerial vehicles (drone). Ocean Coast Manage 182:104952

    Article  Google Scholar 

  • Klein-Hentz ÂM, Kinder PJ, Hubbart JA, Kellner E (2018) Accuracy and optimal altitude for physical habitat assessment (PHA) of stream environments using unmanned aerial vehicles (UAV). Drones 2:20

    Article  Google Scholar 

  • Lu B, He Y (2017) Optimal spatial resolution of unmanned aerial vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem. GISci Remote Sens 55(2):205–220

    Article  Google Scholar 

  • Manfreda S, McCabe MF, Miller PE, Lucas R, Madrigal VP, Mallinis G et al (2018) On the use of unmanned aerial systems for environmental monitoring. Remote Sens 10:641

    Article  Google Scholar 

  • Mury A, Collin A, James D (2019) Morpho-sedimentary monitoring in a costal area, from 1D to 2.5D, using airborne drone imagery. Drones 3:62

    Article  Google Scholar 

  • Oliveira S, Moura D, Boski T, Horta J (2019) Coastal paleokarst landforms: a morphometric approach via UAV for coastal management (Algarve, Portugal case study). Ocean Coast Manage 167:245–261

    Article  Google Scholar 

  • Otsu K, Pla M, Duane A, Cardil A, Brotons L (2019) Estimating the threshold of detection on tree crown defoliation using vegetation indices from UAS multispectral imagery. Drones 3:80

    Article  Google Scholar 

  • Pádua L, Vanko J, Hruŝka J, Adăo T, Sousa JJ, Peres E et al (2017) UAS, sensors, and data processing in agroforestry: a review towards practical applications. Int J Remote Sens 38:2349–2391

    Article  Google Scholar 

  • Pajares G (2015) Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogramm Eng Remote Sens 81(4):281–329

    Article  Google Scholar 

  • Rivas-Casado M, Ballesteros-Gonzalez R, Kriechbaumer T, Veal A (2015) Automated identification of river hydromorphological features using UAV high resolution aerial imagery. Sensors 15:27969–27989

    Article  Google Scholar 

  • Shahbazi M, Théau J, Ménard P (2014) Recent applications of unmanned aerial imagery in natural resource management. GISci Remote Sens 51(4):339–365

    Article  Google Scholar 

  • Singh KK, Frazier AE (2018) A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications. Int J Remote Sens 39:5078–5098

    Article  Google Scholar 

  • Sona G, Pinto L, Pagliari D, Passoni D, Gini R (2014) Experimental analysis of different software packages for orientation and digital surface modelling from UAV images. Earth Sci Info 7(2):97–107

    Article  Google Scholar 

  • Torres-Sánchez J, López-Granados F, Borra-Serrano I, Peña JM (2018) Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards. Precis Agric 19:115–133

    Article  Google Scholar 

  • Verhoeven G (2011) Taking computer vision aloft – archeological three-dimensional reconstructions from aerial photographs with photoscan. Archeol Prospect 18:67–73

    Article  Google Scholar 

  • Watts AC, Ambrosia VG, Hinkley EA (2012) Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens 4:1671–1692

    Article  Google Scholar 

  • Wu D, Li R, Zhang F, Liu J (2019) A review on drone-based harmful algae blooms monitoring. Environ Monit Assess 191:211

    Article  Google Scholar 

  • Wulder MA, White JC, Loveland TR, Woodcock CE, Belward AS, Cohen WB, Fosnight EA, Shaw J, Masek JG, Roy DP (2016) The global Landsat archive: status, consolidation, and direction. Remote Sens Environ 185:271–283

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica under Grant #IA100218, and the Instituto de Ciencias del Mar y Limnología under Grant #622. We thank Fondo Nacional de Fomento al Turismo (FONATUR, Mexico) for the special permission to access private property within the conservation area. FFdS is grateful for field assistance by Luis Alberto Díaz-Lara. Guillermina Fehér edited the English text. Yokinaro Raymundo-Arredondo processed the 120-m height data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francisco Flores-de-Santiago.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Flores-de-Santiago, F., Valderrama-Landeros, L., Rodríguez-Sobreyra, R. et al. Assessing the effect of flight altitude and overlap on orthoimage generation for UAV estimates of coastal wetlands. J Coast Conserv 24, 35 (2020). https://doi.org/10.1007/s11852-020-00753-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11852-020-00753-9

Keywords

Navigation