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1 ESA UNCLASSIFIED - For Official Use

2 OPPORTUNITIES OF UAV BASED SENSING FOR VEGETATION LAND PRODUCT VALIDATION Benjamin Brede 1, Juha Suomalainen 2, Peter Roosjen 1, Helge Aasen 3, Lammert Kooistra 1, Harm Bartholomeus 1, Jan Clevers 1, Martin Herold 1 1 Wageningen University & Research, Laboratory of Geo-Information Science and Remote Sensing, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands 2 Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 1, Masala, Finland 3 Crop Science Lab, Institute of Agricultural Sciences, Federal Institute of Technology Zürich (ETHZ), Switzerland ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 2

3 Overview 1. Background 2. Opportunities 3. Case Studies a. Radiometric Inter-comparison b. Albedo/BRDF: Flying Goniometers c. Biomass: Geometrical modelling with UAV-based lidar 4. Challenges 5. Conclusions ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 3

4 1. Background Popularity of UAVs due to out-of-the-box solutions Democratisation of data acquisition (Aasen & Bolten, 2018) Beginners + expert Users Active community: e.g. COST Action OPTIMISE Web of Science: Number of publications under topic UAV in category remote sensing (as of ) ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 4

5 1. Background Sensor (payload) diversity: VIS/NIR multi-/hyperspectral imaging/line scanner Thermal IR imager Lidar SAR (X/C-band on mini UAV) ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 5

6 2. Opportunities for LPV 1. Support for present cal/val activities 1. Sampling speed + large areas > cost effective 2. Interpolating ground (point) measurements (UAV-RS as covariate) 2. New cal/val strategies 1. Enable mapping (spatially continuous sampling) 2. New sampling perspective (even compared to manned aircraft) 3. Repeated observations > temporal domain ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 6

7 3. Radiometric Inter-comparison HYperspetral Mapping SYstem (HYMSY) (Suomalainen, et al. 2014): nm, 101 bands à 9 nm (FWHM) Spectral simulation of MSI VIS/NIR: ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 7

8 3. Radiometric Inter-comparison 5 flights under clear sky Calibration with Spectralon panel in forest opening S2A processing with sen2cor Inter-comparison shows agreement within ~5% Higher NIR for HYMSY due to calibration in opening ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 8

9 3. Flying Goniometers Multi-angular sampling with super-spectral frame camera Roosjen et al. (2017) See also Burkart et al. (2015) ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 9

10 3. BRDF from Spectral Frame Cameras Inversion of PROSAIL: only nadir vs multi-angle Improved retrieval with multi-angular measurements Roosjen, P. P. J., Brede, B., Suomalainen, J. M., Bartholomeus, H. M., Kooistra, L., & Clevers, J. G. P. W. (2018). Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data - potential of unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, 66, ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 10

11 3. Geometrical modelling Acquisition Speed RIEGL RiCOPTER TLS UAV-LS Brede et al. (2017) ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 11

12 3. Geometrical modelling TLS Acquisition Speed UAV-LS Explicit geometrical modelling with Quantative Structural Models (QSM) (Raumonen et al 2013) Retrieve tree volume and estimate AGB UAV decreases acquisition time ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 12

13 4. Challenges User requirements: Diverse users vs standardisation Knowledge in all fields from spectral data acquisition to data processing Decentralized approach for LPV Legislation ICAO: UAV (RPAS) as regular participants in aviation Push from companies into the market > need for clear (international) rules ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 13

14 5. Conclusions Various platforms, sensors and algorithms/software for UAV RS Active community Potential to support LPV activities in various domains/for various land products Increase sampling speed Allow mapping New sampling pathways ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 14

15 Acknowledgements & Links Benjamin Brede is funded by the IDEAS+ project funded by ESA-ESRIN. Speulderbos Cal/Val Site: Wageningen University & Research Unmanned Aerial Remote Sensing Facility (UARSF): ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 15

16 Thank you for your attention! ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 16

17 References Reviews Aasen, H. (2017). State-of-the-art in UAV remote sensing survey - First insights into applications of UAV sensing systems. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2W6), Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, Stöcker, C., Bennett, R., Nex, F., Gerke, M., & Zevenbergen, J. (2017). Review of the current state of UAV regulations. Remote Sensing, 9(5), Research articles Aasen, H., & Bolten, A. (2018). Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers - from theory to application. Remote Sensing of Environment, 205(September 2017), Brede, B., Lau, A., Bartholomeus, H. M., & Kooistra, L. (2017). Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors, 17(10), Burkart, A., Aasen, H., Alonso, L., Menz, G., Bareth, G., & Rascher, U. (2015). Angular dependency of hyperspectral measurements over wheat characterized by a novel UAV based goniometer. Remote Sensing, 7(1), Roosjen, P. P. J., Suomalainen, J. M., Bartholomeus, H. M., Kooistra, L., & Clevers, J. G. P. W. (2017). Mapping reflectance anisotropy of a potato canopy using aerial images acquired with an unmanned aerial vehicle. Remote Sensing, 9(5). Roosjen, P. P. J., Brede, B., Suomalainen, J. M., Bartholomeus, H. M., Kooistra, L., & Clevers, J. G. P. W. (2018). Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data -- potential of unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, 66, Suomalainen, J., Anders, N., Iqbal, S., Roerink, G., Franke, J., Wenting, P., Kooistra, L. (2014). A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles. Remote Sensing, 6(11), ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 17

18 Reserve slides ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 18

19 3. Support for Classical ESU Sampling Wall-to-Wall ESU (e.g. LAI, fapar) Target field unknown Random sampling (N = 50) Target field approximated by spectral data Stratified sampling (N=5) ESU ESU ESA UNCLASSIFIED - For Official Use Benjamin Brede et al. ESRIN 27/02/2018 Slide 19