To support environmental management there is increasing need for timely, accurate and detailed information on our land. Drones or Unmanned Aerial Vehicles (UAV) are increasingly used to monitor agricultural crop development, habitat quality or urban heat efficiency. An important reason is that UAV technology is maturing quickly while the flexible capabilities of UAV fill a gap between satellite based and ground based geo-sensing systems.
In 2012, different groups within Wageningen University & Research established an Unmanned Airborne Remote Sensing Facility (UARSF). The objective of this facility is threefold:
- To develop innovation in the field of remote sensing science by providing a platform for dedicated and high-quality experiments;
- To support high quality UAS services by providing calibration facilities and disseminating processing procedures to the UAS user community;
- To promote and test the use of UAS in a broad range of application fields like habitat monitoring, precision agriculture and land degradation assessment.
Currently, several groups from Wageningen University and Research take part in the facility: these include the Laboratory of Geo-Information Science and Remote Sensing (GRS) and the chair group Information Technology, Wageningen Food Safety Research, Unifarm and NPEC of Wageningen Plant Research, and the Earth Informatics team of Wageningen Environmental Research.
Description of facility
The added value of the Unmanned Aerial Remote Sensing Facility (UARSF) is that compared to for example satellite-based remote sensing, more dedicated science experiments can be prepared. This includes for example higher frequent observations in time (e.g., diurnal observations), observations of an object under different observation angels for characterization of BRDF and flexibility in use of cameras and sensor types. In this way, laboratory type of set-ups can be tested in a field situation and effects of up-scaling can be tested.
Within the facility a range of UAV platforms are available: these include the following rotor-based systems DJI S1000, DJI M100, DJI M210, DJI Phantom, DJI Mavic, while also we have a fixed system available: the Sensefly Ebee. The choice of a specific platform depends on the requirements of the experiment and capabilities of the platform.
Next to UAV platforms, also a large range of camera systems can be used:
- Hyperspectral mapping system (HYMSY)
- Rikola hyperspectral camera
- Wiris thermal camera
- RGB-NIR camera (MUMSY)
- Several RGB camera’s: including DJI Zenmuse X7
- Sequoia multi-spectral camera
- Fluorescence point-spectrometer (FluorSpec)
- Several chemical sensors to measure gas components and atmospheric particles
Finally, we are operating a Laser Scanning system on a UAV: this is the Riegl Ricopter. from this LiDAR system, detailed and precise 3D models of objects can be collected and mapped.
The following projects are using or have been using the expertise and equipment of the UARSF:
- I-Seeds (H2020; 2021-2024): GRS partner: developing a new generation of self-deployable and biodegradable soft miniaturized robots
- PalmWatch (BELSPO; 2019-2022): GRS partner: Using remote sensing to tackle red palm weevil in palms
- EU project GENTORE (H2020: 2018-2022): WENR: Phenotyping livestock with drones
- KB-NPEC (Kennisbasis 2018-2022). Phenotyping with drones
- Automated Airborne Pest Management (NWO; 2017-2020): GRS partner
- PPS PrecisieTuinbouw ‘Next Generation Phenotyping’ (2017-2020). WENR
- Spectors (Interreg; 2016-2020): GRS en WEnR partner
Relevant PhD projects
- Wan Quanxing (2019-2023): Monitoring crop water stress from local to regional scale: supporting farmland water management.
- Norazlida Jamil (2019-2023): Individual Plant-Growth Monitoring Based On UAV Imaging.
- Chenglong Zhang (2018-2022): Spatial and temporal yield estimation and prediction for orchard management using high-resolution UAV-based imagery technology.
- Na Wang (2017-2021): Measuring sun-induced fluorescence using UAVbased remote sensing.
- Gustavo Togeiro de Alckmin (2016-2020): Compare and contrast approaches of measurement of perennial ryegrass for biophysical and biochemical attributes.
- Marston Domingues Franceschini (2014-2020): Multiple sensor data fusion and time-series analysis for near-real time monitoring and detecting changes in biophysical and chemical crop conditions.
Relevant links education
Open source UAV datasets
- Wageningen (NL): UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture.
- Speulderbos (NL): Terrestrial (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAV-LS) 2017.
Characterising Termite Mounds in a Tropical Savanna with UAV Laser ScanningRemote Sensing 13 (2021)3. - ISSN 2072-4292
Diurnal variation of sun-induced chlorophyll fluorescence of agricultural crops observed from a point-based spectrometer on a UAVInternational Journal of applied Earth Observation and Geoinformation 96 (2021). - ISSN 0303-2434
Effectiveness of soil erosion barriers to reduce sediment connectivity at small basin scale in a fire-affected forestJournal of Environmental Management 278 (2021). - ISSN 0301-4797
Pilot innovatieve inwinning zeegras Oosterschelde
Automated crop plant counting from very high-resolution aerial imageryPrecision Agriculture 21 (2020). - ISSN 1385-2256 - p. 1366 - 1384.
Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indicesPrecision Agriculture 22 (2021). - ISSN 1385-2256 - p. 205 - 225.
Deep learning for automated detection of Drosophila suzukii : potential for UAV-based monitoringPest Management Science 76 (2020)9. - ISSN 1526-498X - p. 2994 - 3002.
Experimental flight patterns evaluation for a UAV-based air pollutant sensorMicromachines 11 (2020)8. - ISSN 2072-666X
MOOC drones for agriculture : The making-ofIn: Proceedings of the 2020 IEEE Global Engineering Education Conference, EDUCON 2020. - : IEEE computer society (IEEE Global Engineering Education Conference, EDUCON ) - ISBN 9781728109312 - p. 1692 - 1695.
UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agricultureODjAR : open data journal for agricultural research 6 (2020). - ISSN 2352-6378 - p. 1 - 7.
Biomass and crop height estimation of different crops using UAV-based LiDARRemote Sensing 12 (2019)1. - ISSN 2072-4292
Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV ImageryRemote Sensing 12 (2020)1. - ISSN 2072-4292 - 26 p.
A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning TechniqueFrontiers in Plant Science 11 (2020). - ISSN 1664-462X
An open simulation strategy for rapid control design in aerial and maritime drone teams : A comprehensive tutorialDrones 4 (2020)3. - ISSN 2504-446X - 20 p.
Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point CloudsDrones 3 (2019)3. - ISSN 2504-446X
Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDARRemote Sensing of Environment 233 (2019). - ISSN 0034-4257
Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop ProductivityDrones 3 (2019)3. - ISSN 2504-446X
Fast Classification of Large Germinated Fields Via High-Resolution UAV ImageryIEEE Robotics and Automation Letters 4 (2019)4. - ISSN 2377-3766 - p. 3216 - 3223.
Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in PotatoRemote Sensing 11 (2019)3. - ISSN 2072-4292 - 47 p.
A Comprehensive Study of the Potential Application of Flying Ethylene-Sensitive Sensors for Ripeness Detection in Apple OrchardsSensors 19 (2019)2. - ISSN 1424-8220
Editorial of special issue “drones for biodiversity conservation and ecological monitoring”Drones 3 (2019)2. - ISSN 2504-446X - p. 1 - 4.
Automatic apple tree blossom estimation from uav rgb imageryIn: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - : ISPRS (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ) - p. 631 - 635.
Geometric Tree Modelling with UAV-based Lidar
Opportunities of UAV based Sensing for Vegetation Land Product Validation
Assessment of Potato Disease Infestation Using Combined Sources Of High-Resolution UAV Imagery
Inter-row Weed Detection of Sugar Beet Fields Using Aerial Imagery
UAV-imaging to model growth response of marram grass to sand burial : Implications for coastal dune developmentAeolian Research 31 (2018). - ISSN 1875-9637 - p. 50 - 61.
Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data – potential of unmanned aerial vehicle imageryInternational Journal of applied Earth Observation and Geoinformation 66 (2018). - ISSN 0303-2434 - p. 14 - 26.
Automated Airborne Pest Monitoring of Drosophila suzukii in Crops and Natural Habitats
Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forestsInterface Focus 8 (2018)2. - ISSN 2042-8898
Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral ImageryRemote Sensing 10 (2018)10. - ISSN 2072-4292 - 25 p.
The Electronic Smell of the Orchard Fruit
Field phenotyping of maize growth parameters using UAV acquired high-resolution hyperspectral images
Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensorsBiogeosciences 14 (2017)3. - ISSN 1726-4170 - p. 733 - 749.
Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and PotatoRemote Sensing 8 (2016)11. - ISSN 2072-4292 - 16 p.