The surface modelling based on UAV Photogrammetry and qualitative estimation

Ruzgienė, Birutė; Berteška, Tautvydas; Gecyte, Silvija; Jakubauskienė, Edita; Aksamitauskas, Vladislovas Česlovas

doi:10.1016/j.measurement.2015.04.018

Cited by 125 publications

(79 citation statements)

References 15 publications

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“…In addition, the abovementioned height differences characterizing the different microforms are very small (e.g., variation at a centimeter scale matters; see Figure 2) and require an extremely accurate surface model for a successful integration into the classification process. There is an ongoing discussion in recent research articles about the achievable accuracy of surface models created by UAS based stereo photogrammetric analysis [52][53][54][55]. Thus, it is unclear if this achievable accuracy is sufficient for ecosystems, where the morphology is characterized by slight surface differences.…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

Lehmann¹,

Münchberger²,

Knoth

et al. 2016

Remote Sensing

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South Patagonian peat bogs are little studied sources of methane (CH 4 ). Since CH 4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH 4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH 4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species-and 0.83 for the microform-level classification, respectively. CH 4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH 4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH 4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg¨m´2¨day´1 to 26.8 mg¨m´2¨day´1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH 4 fluxes when plot-scale fluxes are up-scaled.

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Section: Discussionmentioning

confidence: 99%

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

Lehmann¹,

Münchberger²,

Knoth

et al. 2016

Remote Sensing

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“…Taking into consideration the surveying technique used and the planar coordinate system EPSG:2177 (the one applicable in Poland, used in geodetic works) (Bosy 2014), it was decided that all the measurements made would be defined in this system for practical reasons. Due to the fact that UAV-obtained images are often oblique, it is recommended that a great number of control points be established in the surveyed area (Ruzgien_ e et al 2015). Taking into account the size of the studied object and the planned altitude of the flight, four targeted GCPs were established (Fig.…”

Section: Establishing Ground Controlmentioning

confidence: 99%

Geovisualisation of Relief in a Virtual Reality System on the Basis of Low-Level Aerial Imagery

Halik

Smaczyński

2017

Pure Appl. Geophys.

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Abstract-The aim of the following paper was to present the geomatic process of transforming low-level aerial imagery obtained with unmanned aerial vehicles (UAV) into a digital terrain model (DTM) and implementing the model into a virtual reality system (VR). The object of the study was a natural aggretage heap of an irregular shape and denivelations up to 11 m. Based on the obtained photos, three point clouds (varying in the level of detail) were generated for the 20,000-m 2 -area. For further analyses, the researchers selected the point cloud with the best ratio of accuracy to output file size. This choice was made based on seven control points of the heap surveyed in the field and the corresponding points in the generated 3D model. The obtained several-centimetre differences between the control points in the field and the ones from the model might testify to the usefulness of the described algorithm for creating large-scale DTMs for engineering purposes. Finally, the chosen model was implemented into the VR system, which enables the most lifelike exploration of 3D terrain plasticity in real time, thanks to the first person view mode (FPV). In this mode, the user observes an object with the aid of a Head-mounted display (HMD), experiencing the geovisualisation from the inside, and virtually analysing the terrain as a direct animator of the observations.

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“…This circumstance can cause a reduction in the percentage of forward-lap and side-lap, among other factors. The consequence is that the SfM algorithms used to process UAV flights do not work as well as they should [54]. Moreover, the error range considering altitude AGL is lower flying in cruising mode than in stop mode.…”

Section: Assessment Of Spectral Discriminationmentioning

confidence: 99%

Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management

et al. 2015

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This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV) to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m), two flight modes (stop and cruising modes) and two ground control point (GCP) settings were considered to analyze the influence of these parameters on the spatial resolution and spectral discrimination of multispectral orthomosaicked images obtained using Pix4Dmapper. Moreover, it is also necessary to consider the area to be covered or the flight duration according to any flight mission programmed. The effect of the combination of all these parameters on the spatial resolution and spectral discrimination of the orthomosaicks is presented. Spectral discrimination has been evaluated for a specific agronomical purpose: to use the UAV remote images for the detection of bare soil and vegetation (crop and weeds) for in-season site-specific weed management. These results show that a balance between spatial resolution and spectral discrimination is needed to optimize the mission planning and image processing to achieve OPEN ACCESS Remote Sens. 2015, 7 12794 every agronomic objective. In this way, users do not have to sacrifice flying at low altitudes to cover the whole area of interest completely.

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The surface modelling based on UAV Photogrammetry and qualitative estimation

Cited by 125 publications

References 15 publications

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

Geovisualisation of Relief in a Virtual Reality System on the Basis of Low-Level Aerial Imagery

Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management

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