Articles | Volume 12, issue 1
https://doi.org/10.5194/bg-12-163-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-12-163-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Deploying four optical UAV-based sensors over grassland: challenges and limitations
S. K. von Bueren
Institute of Agriculture & Environment, Massey University, Palmerston North, New Zealand
A. Burkart
CORRESPONDING AUTHOR
Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
A. Hueni
Remote Sensing Laboratories, University of Zurich, Zurich, Switzerland
U. Rascher
Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
M. P. Tuohy
Institute of Agriculture & Environment, Massey University, Palmerston North, New Zealand
I. J. Yule
Institute of Agriculture & Environment, Massey University, Palmerston North, New Zealand
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Cited
113 citations as recorded by crossref.
- VARIABILIDADE ESPACIAL E TEMPORAL DO ÍNDICE VEGETAÇÃO MPRI APLICADO ÀS IMAGENS DE GRAMA SÃO CARLOS OBTIDAS POR AERONAVE REMOTAMENTE PILOTADA L. Gonçalves et al. 10.18011/bioeng2017v11n4p340-349
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111 citations as recorded by crossref.
- VARIABILIDADE ESPACIAL E TEMPORAL DO ÍNDICE VEGETAÇÃO MPRI APLICADO ÀS IMAGENS DE GRAMA SÃO CARLOS OBTIDAS POR AERONAVE REMOTAMENTE PILOTADA L. Gonçalves et al. 10.18011/bioeng2017v11n4p340-349
- Replacing Manual Rising Plate Meter Measurements with Low-cost UAV-Derived Sward Height Data in Grasslands for Spatial Monitoring G. Bareth & J. Schellberg 10.1007/s41064-018-0055-2
- Techniques, Answers, and Real-World UAV Implementations for Precision Farming A. Srivastava & J. Prakash 10.1007/s11277-023-10577-z
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- Spectral band selection for vegetation properties retrieval using Gaussian processes regression J. Verrelst et al. 10.1016/j.jag.2016.07.016
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- Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot A. de la Casa et al. 10.1016/j.isprsjprs.2018.10.018
- Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization T. Hakala et al. 10.3390/s18051417
- Calibration and Validation from Ground to Airborne and Satellite Level: Joint Application of Time-Synchronous Field Spectroscopy, Drone, Aircraft and Sentinel-2 Imaging P. Naethe et al. 10.1007/s41064-022-00231-x
- Chip-scale short-wavelength infrared InGaAs microspectrometer based on a linear variable optical filter J. Jeon et al. 10.1039/D3TC01239E
- A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems L. G. Poley & G. J. McDermid 10.3390/rs12071052
- Low-cost unmanned aerial vehicle-based digital hemispherical photography for estimating leaf area index: a feasibility assessment L. Brown et al. 10.1080/2150704X.2020.1802527
- Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping M. Pérez-Bueno et al. 10.3389/fpls.2016.01790
- Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows H. Aasen et al. 10.3390/rs10071091
- Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines K. Johansen et al. 10.1016/j.jclepro.2018.10.287
- A feature-supervised generative adversarial network for environmental monitoring during hazy days K. Wang et al. 10.1016/j.scitotenv.2020.141445
- Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance H. Aasen et al. 10.1016/j.isprsjprs.2015.08.002
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- Spatial and Temporal Pasture Biomass Estimation Integrating Electronic Plate Meter, Planet CubeSats and Sentinel-2 Satellite Data J. Gargiulo et al. 10.3390/rs12193222
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- Small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenges and opportunities revisited P. Hardin et al. 10.1080/15481603.2018.1510088
- Reflectance calibration of UAV-based visible and near-infrared digital images acquired under variant altitude and illumination conditions L. Teixeira Crusiol et al. 10.1016/j.rsase.2020.100312
- Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications J. Kim et al. 10.1109/ACCESS.2019.2932119
- Advance control strategies using image processing, UAV and AI in agriculture: a review I. Syeda et al. 10.1108/WJE-09-2020-0459
- Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects T. Xiang et al. 10.1109/MGRS.2019.2918840
- An Approach for Route Optimization in Applications of Precision Agriculture Using UAVs K. Srivastava et al. 10.3390/drones4030058
- A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming N. Islam et al. 10.3390/su13041821
- Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery B. Lu et al. 10.1080/01431161.2017.1363441
- Role of Unmanned Aerial Systems for Natural Resource Management P. Mishra & A. Rai 10.1007/s12524-020-01230-4
- Commercial Off-the-Shelf Digital Cameras on Unmanned Aerial Vehicles for Multitemporal Monitoring of Vegetation Reflectance and NDVI E. Berra et al. 10.1109/TGRS.2017.2655365
- The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures E. Grüner et al. 10.1371/journal.pone.0234703
- The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle M. Barbosa Júnior et al. 10.3390/drones6050112
- Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation H. Anderson et al. 10.3390/rs8100847
- A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications K. Singh & A. Frazier 10.1080/01431161.2017.1420941
- Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer Z. Sha et al. 10.1093/jpe/rty036
- Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor P. Olsson et al. 10.3390/rs13040577
- A review on drone-based harmful algae blooms monitoring D. Wu et al. 10.1007/s10661-019-7365-8
- UAV LiDAR-based grassland biomass estimation for precision livestock management C. Hütt et al. 10.1117/1.JRS.18.017502
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Short summary
Unmanned aerial vehicles (UAVs) equipped with optical sensors facilitate non-invasive, real-time vegetation analysis. To guarantee robust scientific analysis, protocols need to be developed and sensors must be compared to state-of-the-art instruments. Here we compare four UAV sensors (RGB, NIR, six-band, spectrometer) to evaluate their applicability for vegetation monitoring. By showing the opportunities and pitfalls of UAV-based sensing, we describe ways to gather sound scientific data.
Unmanned aerial vehicles (UAVs) equipped with optical sensors facilitate non-invasive, real-time...
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