Presentation given for America View on October 11, 2019

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%
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1990
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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

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§ 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.
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