Use of UAS for Hydrological Monitoring
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This document discusses the use of unmanned aerial systems (UAS) for hydrological monitoring. It describes how UAS can be used to monitor vegetation status, streamflow, flooded areas, and river morphology from the plot scale to the river basin scale. It outlines the COST Action HARMONIOUS, which aims to promote UAS monitoring strategies and establish harmonized practices through a global network of over 200 researchers from 36 countries. The document provides examples of UAS applications for monitoring vegetation stress, soil moisture, and streamflow and discusses efforts to standardize UAS data processing and analysis methods across different environments.
![18
SMAR-EnKF
optimization
and prediction
Root mean square errors
ranging from 0.014 -
0.049 [cm3 cm-3].
Semi-arid Highlands
Temperate Forests
Temperate Forests
North American Deserts
Great PlainsTropical Wet Forests
Forested Mountains
Northern Forests
(Baldwin et al., J. Hydr., 2017)](https://image.slidesharecdn.com/2019manfredaamericaviewcompressed-191014150827/85/Use-of-UAS-for-Hydrological-Monitoring-18-320.jpg)
Use of UAS for Hydrological Monitoring
- 1. Use of UAS for Hydrological Monitoring America View – 11 October 2019 Prof. Salvatore Manfreda Associate Professor of Water Management and Ecohydrology - https://www.salvatoremanfreda.it Chair of the COST Action Harmonious - http://www.costharmonious.eu
- 2. 2 Unmanned Arial Systems (UAS) in Hydrology Scope: state of the vegetation, streamflow (speed and water level), extension of the flooded areas and morphology. Objective: To define integrated procedures to improve hydrological/hydraulic monitoring capacity using UAS. Scale: from plot-scale to the river basin scale providing operational monitoring tools.
- 3. Environmental Monitoring Up to 30cm Up to 1cm Up to 1cm Comparable Scales Few hectars Few m2 Global
- 4. 4Manfreda et al. (Remote Sensing, 2018) UAS vs Satellite
- 5. 5 Number of articles extracted from the database ISI web of knowledge 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 500 1000 1500 2000 2500 3000 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Percentage of Environmental Studies (%) Number of Papers Year UAS applications Environmental Applications Percentage of Environmental Studies from 1990 up to 2017 (last access 15/01/2018) automation of a single or multiple vehicles, tracking and flight control systems, hardware and software innovations, tracking of moving targets, image correction and mapping performance assessment
- 6. 6 A network of scientists is currently cooperating within the framework of a COST (European Cooperation in Science and Technology) Action named “HARMONIOUS”. The intention of “HARMONIOUS” is to promote monitoring strategies, establish harmonized monitoring practices, and transfer most recent advances on UAS methodologies to others within a global network. COST Action HARMONIOUS https://www.costharmonious.eu
- 7. 7 HARMONIOUS Partners COST Countries 216 Researchers 36 Countries involved Total budget 780k€ Period November 2017 – November 2021 Activities Promoted Workshops Working Group Meeting Training Courses STSMs Numbers of the COST Action Harmonious https://www.costharmonious.eu
- 8. 8 Contrast Enhancement Geometric Correction and image calibration WG3 Soil Moisture Content Leader Zhongbo Su Vice leader David Helman Stream flow River morphology WG2 Vegetation Status Leader Antonino Maltese Vice leader Felix Frances Harmonization of different procedures and algorithms in different environments WG1: UAS data processing Leader Pauline Miller Vice leader Victor Pajuelo Madrigal WG5: Harmonization of methods and results Leader Eyal Ben Dor Vice leader Flavia Tauro WG4 Leader Matthew Perks Vice leader Marko Kohv Action Chair Salvatore Manfreda Vice Chair Brigitta Toth Science Communications Manager: Guiomar Ruiz Perez STSM coordinator: Isabel De Lima Training School Coordinator: Giuseppe Ciraolo HARMONIOUS Action https://www.costharmonious.eu https://www.costharmonious.eu
- 9. 9 WG5: Harmonization of different procedures and algorithms in different environments WG4: River and Streamflow monitoring WG3: Soil Moisture Monitoring WG2: Vegetation Monitoring WG1: Data Collection, Processing and Limitations b) Identification of the shared problems a) Peculiarities and specificity of each topic c) Identification of possible common strategies for the four WGs d) Definition of the correct protocol fro UAS Environmental Monitoring https://www.costharmonious.eu
- 10. 10 Examples of Common image artifacts (Whitehead and Hugenholtz, 2014) a) saturated image; b) vignetting; c) chromatic aberration; d) mosaic blurring in overlap area; e) incorrect colour balancing; f) hotspots on mosaic due to bidirectional reflectance effects; g) relief displacement (tree lean) effects in final image mosaic; h) Image distortion due to DSM errors; i) mosaic gaps caused by incorrect orthorectification or missing images. WG1: UAS data processing
- 11. 11 Comparison between a CubeSat and UAS NDVI map Multi-spectral false colour (near infrared, red, green) imagery collected over the RoBo Alsahba date palm farm near Al Kharj, Saudi Arabia. Imagery (from L-R) shows the resolution differences between: (A)UAS mounted Parrot Sequoia sensor at 50 m height (0.05 m); (B) WorldView-3 image (1.24 m); (C) Planet CubeSat data (approx. 3 m), collected on the 13th, 29° and 27th March 2018, respectively. WG2 Vegetation Status
- 12. 12 UAS thermal survey over an Aglianico vineyard in the Basilicata region (southern Italy) WG2 Vegetation Status (Manfreda et al., R.S. 2018a)
- 13. 13 How to detect water stress from an UAV? From Xurxo Gago
- 14. 14 Aerial thermography for water stress detection (Berni et al., 2009)
- 15. 15 WG3 Soil Moisture ¹ 0 20 40 60 8010 Meters Soil Moisture Monitoring
- 16. 16 Relationship existing between surface and root-zone soil moisture Manfreda et al. (AWR - 2007) Developing a relationship between the relative soil moisture at the surface to that in deeper layers of soil would be very useful for remote sensing applications. This implies that prediction of soil moisture in the deep layer given the superficial soil moisture, has an uncertainty that increases with a reduced near surface estimate.
- 17. 17 Soil Moisture Analytical Relationship (SMAR) The schematization proposed assumes the soil composed of two layers, the first one at the surface of a few centimeters and the second one below with a depth assumed coincident with the rooting depth of vegetation (of the order of 60–150 cm). This allowed the derivation of SMAR Manfreda et al. (HESS - 2014) s1(t) s2(t) First layer Second layer Zr2 Zr1 !! !! = !! + (!! !!!! − !!)!!!! !!!!!!! + 1 − !! !!! !! !! − !!!!
- 18. 18 SMAR-EnKF optimization and prediction Root mean square errors ranging from 0.014 - 0.049 [cm3 cm-3]. Semi-arid Highlands Temperate Forests Temperate Forests North American Deserts Great PlainsTropical Wet Forests Forested Mountains Northern Forests (Baldwin et al., J. Hydr., 2017)
- 19. 19 The Experimental Field Site of Monteforte (SA) WG3 Soil Moisture
- 20. 20 Optic/thermal sensors Radar sensors )" = $ % − %' ((% − %' di Non contact equipments Advantages 1) High spatial and temporal resolution 2) Relatively low costs 3) Applicable inaccessible sections WG3 Stream flow
- 21. 21 Stream flow monitoring with UAS Particle Tracking Velocimetry (PTV) Image processing WG4: Stream Monitoring Lagrangian method (Tauro et al., 2016)
- 22. 22 Channel Morphology One of the most mature applications of optical sensing from UAS is the use of computer vision approaches (i.e. structure-from-motion) to reconstruct three-dimensional surfaces, allowing previously unheard of resolutions and accuracies that can inform the production of digital surface and elevation models. (Manfreda and McCabe, Hydrolink 2019)
- 23. 23 Monitoring River Systems (Dal Sasso et al., E.M.A. 2018)
- 24. 24 Optimal parameter settings for PTV techniques Box plot of the relative error for the different densities investigated in the configurations: a ideal condition, b real condition (Dal Sasso et al., E.M.A. 2018) Particle displacement: Dx=1.5Dxp – Number of frames: 20 Ideal configuration Real configuration 0 1 2 3 4 5 6 x 10 -4 0 200 400 600 800 1000 1200 1400 Seeding density (ppp) N.frames fitted curve (y= 0.0002635x-1.514 ) fitted curve (y= 0.0001318x-1.514 ) fitted curve (y= 0.0000659x-1.514 ) Numerical experiments (Dx=1.5Dxp) Numerical experiments (Dx=3Dxp) Numerical experiments (Dx=6Dxp)
- 25. 25 Image Velocimetry Techniques: Example of application
- 26. 26 2-D flow velocity field derived using an optical camera mounted on a quadcopter hovering over a portion of the Bradano river system in southern Italy. One of the images used for the analysis is shown as a background, where surface features used by flow tracking algorithms are highlighted in the insets (a, b). Image Velocimetry
- 27. 27 Stream Flow Monitoring – Data Collection for Benchmarking Optical Techniques Paper: https://www.earth-syst-sci-data-discuss.net/essd-2019-133/ Data: https://doi.org/10.4121/uuid:34764be1-31f9-4626-8b11-705b4f66b95a Image velocimetry data available for 12 case studies
- 28. 28 § UAS-based remote sensing provides new advanced procedures to monitor key variables, including vegetation status, soil moisture content, and stream flow. § The detailed description of such variables will increase our capacity to describe water resource availability and assist agricultural and ecosystem management. § The wide range of applications testifies to the great potential of these techniques, but, at the same time, the variety of methodologies adopted is evidence that there is still need for harmonization efforts. § HARMONIOUS COST Action aims to build standardized protocols for UAS- based applications to improve reliability of such technology. Conclusion
- 29. 29
- 30. 30 § Tmusic, Manfreda, Aasen, James, Concalves, Ben-Dor, Brook, Polinova, Juan Arranz, Pedroso de Lima, Meszaros, Zhuang, Davis, Herban, Malbeteau, Matthew, Practical guidance to UAS based environmental monitoring, Remote Sensing (in preparation). § Manfreda and McCabe (2019). Emerging earth observing platforms offer new insights into hydrological processes, Hydrolink. § Perks, Hortobágyi, Le Coz, Maddock, Pearce, Tauro, Dal Sasso, Grimaldi, Manfreda (2019) Towards harmonization of image velocimetry techniques for determining open-channel flow, Earth system science data Earth System Science Data Discussion. § Manfreda, Dvorak, Mullerova, Herban, Vuono, Arranz Justel, Perks (2019) Assessing the Accuracy of Digital Surface Models Derived from Optical Imagery Acquired with Unmanned Aerial Systems, Drones. § Manfreda (2018), On the derivation of flow rating-curves in data-scarce environments, Journal of Hydrology. § Dal Sasso, Pizarro, Samela, Mita, and Manfreda (2018) Exploring the optimal experimental setup for surface flow velocity measurements using PTV, Environmental Monitoring and Assessment. § Manfreda, McCabe, Miller, Lucas, Pajuelo Madrigal, Mallinis, Ben-Dor, Helman, Estes, Ciraolo, Müllerová, Tauro, De Lima, De Lima, Frances, Caylor, Kohv, Maltese (2018), On the Use of Unmanned Aerial Systems for Environmental Monitoring, Remote Sensing. § Baldwin, Manfreda, Keller, and Smithwick (2017), Predicting root zone soil moisture with soil properties and satellite near-surface moisture data at locations across the United States, Journal of Hydrology. § Manfreda, Brocca, Moramarco, Melone, and Sheffield (2014), A physically based approach for the estimation of root-zone soil moisture from surface measurements, Hydrology and Earth System Sciences. Related Publications