Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance

By Helge Aasen, Andreas Burkart, Andreas Bolten, Georg Bareth. 2015

444 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/535c3c1cab679534316b3dd58b0a47da40af00ad

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Citations in the corpus

Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems (2019, background): “Although this hurdle is beginning to be overcome [42], often with the application of off-the-shelf consumer electronics components [47,50], there still typically remains a trade-off between sensor size and data quality in these next generation units [40,77,78].”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, methodology): “The hyperspectral information, combined with other modality data, such as data obtained with the DSM, can be used for more accurate classiﬁcation and plant phenology in agricultural applications with a much higher spatial / temporal resolution [34].”
Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes (2018, background): “…errors caused by three main sources 1) differences among sensors and sensor units, 2) changes in ambient light (weather and position of sun), and 3) spatially-constraining the imagery (Kelcey and Lucieer 2012, Turner et al. 2014, Salamí et al. 2014, Aasen et al. 2015, Pádua et al. 2017).”
Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows (2018, methodology): “, a higher resolution panchromatic image [43].”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “A method to derive 3D hyperspectral information from lightweight snapshot sensors on board of UAVs for vegetation monitoring was proposed in [142], based on a structure from motion approach that results in a digital surface model (DSM) as a representation of the 3D space linked to objects’ reflectan”
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, background): “Although progress has been made in spectral remote 537 sensing from UAS (Aasen et al., 2015; Yuan et al., 2017), gap fraction theory with uncalibrated RGB 538 images has its clear advantages considering that spectral cameras are more expensive and calibration 539 is challenging (Bareth et al.”
High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing (2016, methodology): “077 m) but this approach is more consistent and spatially extensive, reducing user error associated with the ruler measurement [35,44].”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “Radiative transfer models can be act as useful tools for correcting angular influences over vegetated environments (Burkart et al., 2015).”
Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications (2019, background): “[76] performed a study to apply 3D data collected from lightweight snapshot cameras attached to aerial vehicles (see Figure 11).”