Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions

Moudrý, Vítězslav; Urban, R.; Štroner, Martin; Komárek, Jan; Brouček, Josef; Prošek, Jiří

doi:10.1080/01431161.2018.1516311

Cited by 30 publications

(20 citation statements)

References 37 publications

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“…Leaf-off DAP data have been shown to be useful in tree species classification [66], terrain characterization [67], and individual tree detection [68]. Except for Bohlin et al [42], who showed that leaf-off DAP data had a large impact on estimating deciduous forest attributes of well-managed conifer-dominated forests, leaf-off DAP has not been used for forest attribute estimation.…”

Section: Leaf-off Aerial Imagery For Forest Attribute Estimation In Umentioning

confidence: 99%

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

2019

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Scientifically robust yet economical and efficient methods are required to gather information about larger areas of uneven-aged forest resources, particularly at the landscape level, to reduce deforestation and forest degradation and to support the sustainable management of forest resources. In this study, we examined the potential of digital aerial photogrammetry (DAP) for assessing uneven-aged forest resources. Specifically, we tested the performance of biomass estimation by varying the conditions of several factors, e.g., image downscaling, vegetation metric extraction (point cloud- and canopy height model (CHM)-derived), modeling method ((simple linear regression (SLR), multiple linear regression (MLR), and random forest (RF)), and season (leaf-on and leaf-off). We built dense point clouds and CHMs using high-resolution aerial imagery collected in leaf-on and leaf-off conditions of an uneven-aged mixed conifer–broadleaf forest. DAP-derived vegetation metrics were then used to predict the dominant height and living biomass (total, conifer, and broadleaf) at the plot level. Our results demonstrated that image downscaling had a negative impact on the accuracy of the dominant height and biomass estimation in leaf-on conditions. In comparison to CHM-derived vegetation metrics, point cloud-derived metrics performed better in dominant height and biomass (total and conifer) estimations. Although the SLR (%RMSE = 21.1) and MLR (%RMSE = 18.1) modeling methods produced acceptable results for total biomass estimations, RF modeling significantly improved the plot-level total biomass estimation accuracy (%RMSE of 12.0 for leaf-on data). Overall, leaf-on DAP performed better in total biomass estimation compared to leaf-off DAP (%RMSE of 15.0 using RF modeling). Nevertheless, conifer biomass estimation accuracy improved when leaf-off data were used (from a %RMSE of 32.1 leaf-on to 23.8 leaf-off using RF modeling). Leaf-off DAP had a negative impact on the broadleaf biomass estimation (%RMSE > 35% for SLR, MLR, and RF modeling). Our results demonstrated that the performance of forest biomass estimation for uneven-aged forests varied with statistical representations as well as data sources. Thus, it would be appropriate to explore different statistical approaches (e.g., parametric and nonparametric) and data sources (e.g., different image resolutions, vegetation metrics, and leaf-on and leaf-off data) to inform the interpretation of remotely sensed data for biomass estimation for uneven-aged forest resources.

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Section: Leaf-off Aerial Imagery For Forest Attribute Estimation In Umentioning

confidence: 99%

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

2019

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“…Considering the methodology of image processing and generation of the point cloud, this method is most suitable for matte surfaces (which reflects the incident light only to a minimum) with a sufficiently variable texture [24][25][26]. For surfaces with simple, uniform texture, as well as shiny or transparent surfaces (even partially), the resulting point cloud could be noisy or even "empty.…”

Section: Structure From Motion Photogrammetrymentioning

confidence: 99%

Modelling the Surface of Racing Vessel’s Hull by Laser Scanning and Digital Photogrammetry

et al. 2019

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The knowledge of the hull shape and geometry of a racing vessel is one of the most important factors for predicting boat performance. The Offshore Racing Congress (ORC) rating system specifies the calculation parameters of the hydrodynamic forces of boat lift and drag on the basis of input data as the length of waterline while sailing, displacement, wetted surface and the volume distribution along the hull. It is represented by sophisticated calculations for national as well as international events and races. Measurement using a reflectorless total station in a coordinate system defined by the sailboat hull is the most established method approved by the ORC organisation. The determination of these geometric parameters by new, unconventional technologies, which should provide a quicker and more detailed measurement while preserving the quality and accuracy of results necessary for the handicap calculations was our main objective. Geometrical shapes of a cabin sailboat hull were determined by the technology of terrestrial laser scanning and two methods of digital close-range photogrammetry—convergence case of photogrammetry and Structure-from-Motion (SfM) method. High-Definition Surveying (HDS) targets for laser scanning and coded targets for digital photogrammetry were used throughout all methods in order to transform the resulting data into a single local coordinate system. The resulting models were mutually compared by visual, geometrical and statistical comparison. In conclusion, both technologies were considered suitable, however, with various advantages and disadvantages. Nevertheless, although labour intensive, the SfM photogrammetry can be considered the most suitable method if the correct procedures are followed.

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“…The use of unmanned aerial vehicles (UAVs) as platforms to collect high-resolution multi-spectral imagery is increasing rapidly in a wide variety of environmental and geographical studies including in agriculture [1][2][3], forestry [4][5][6], ecology [7][8][9], mining [10][11][12], coastal assessments [13][14][15], and fluvial surveys [16][17][18]. In agriculture, obtaining the accurate and timely status of crop growth, such as canopy greenness, leaf area, water stress estimation, and various geographic features, including crop area and digital surface models (DSMs), especially measured systematically over the growing season, is of great interest to plant breeders and agronomists.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Mobile Ground Control Point Improves Accuracy of Agricultural Remote Sensing through Collaboration with UAV

et al. 2020

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Ground control points (GCPs) are critical for agricultural remote sensing that require georeferencing and calibration of images collected from an unmanned aerial vehicles (UAV) at different times. However, the conventional stationary GCPs are time-consuming and labor-intensive to measure, distribute, and collect their information in a large field setup. An autonomous mobile GCP and a collaboration strategy to communicate with the UAV were developed to improve the efficiency and accuracy of the UAV-based data collection process. Prior to actual field testing, preliminary tests were conducted using the system to show the capability of automatic path tracking by reducing the root mean square error (RMSE) for lateral deviation from 34.3 cm to 15.6 cm based on the proposed look-ahead tracking method. The tests also indicated the feasibility of moving reflectance reference panels successively along all the waypoints without having detrimental effects on pixel values in the mosaicked images, with the percentage errors in digital number values ranging from −1.1% to 0.1%. In the actual field testing, the autonomous mobile GCP was able to successfully cooperate with the UAV in real-time without any interruption, showing superior performances for georeferencing, radiometric calibration, height calibration, and temperature calibration, compared to the conventional calibration method that has stationary GCPs.

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Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions

Cited by 30 publications

References 37 publications

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

Modelling the Surface of Racing Vessel’s Hull by Laser Scanning and Digital Photogrammetry

Autonomous Mobile Ground Control Point Improves Accuracy of Agricultural Remote Sensing through Collaboration with UAV

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