This document summarizes work done as part of the COST Action HARMONIOUS network to promote the use of unmanned aerial systems (UAS) for environmental monitoring. It provides an overview of the working groups and their activities, including data processing techniques, vegetation and soil moisture monitoring, and streamflow monitoring using UAS. Results are presented from various field studies analyzing factors like ground control point (GCP) density and distribution, and the effects of multiple flight combinations on 3D model accuracy. The aim of the network is to establish standardized and harmonized UAS monitoring practices for transferring knowledge within the community.

1. HARMONIOUS
UAS Techniques for Environmental Monitoring
Salvatore Manfreda – Mashhad 15 October 2018
salvatore.manfreda@unibas.it

2. Environmental Monitoring
Up to 30cm Up to 1cm Up to 1cm
Comparable Scales
Few hectars Few m2Global

3. 3
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ISI web of knowledge
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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

4. 4Manfreda et al. (Remote Sensing, 2018a)
UAS vs Satellite

5. (Urs Treier)

6. (Anderson & Gaston, 2013)
DRONES

7. TOP APPLICATIONS
Precision agriculture: management of crops to guarantee efficiency of
inputs like water and fertilizer and maximize productivity, quality, and
yield. It also involves the minimization of pests, unwanted flooding, and
disease.
Energy, mining, and utilities: resources management and research
requires monitoring over large territories, often in inaccessible areas.
Real estate, construction, and land development: need managing and
mapping large portion of land or collections of buildings.
Environmental monitoring: ecological state of ecosystems, plant stress,
water pollution, soil contamination, water contamination, monitoring
of water systems (rivers, lakes, dams etc.).

8. Engines
MK3638
Li-Po Battery
8Amp 30C
Camera mount
servo-stabilized
carbon-fiber
UAVEurope ®
Example: multi-copter 6 engines equipped with a
thermal camera
Thermal camera
GOBI384 Xenics®!
Ubiquiwifi ,
on line wi-fi
data streaming
Propellers
(APC 12x3,8 inc)Frame
(carbon fiber Air-
Sci UAVEurope®)
Electronic
systems:
Mikrokopter®

9. Skywalker
HydroLAB Equipment
Phantom 3 pro
Portable radar
Uncooled LWIR Thermal ADC Snap Camera

13. Evolution of a
fungal pathogen
14 May 2014 (from Lyndon Estes, 2015)

14. Evolution of a
fungal pathogen
(from Lyndon Estes, 2015)25 July 2014

15. COST Action
HARMONIOUS
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.
15

16. 16
HARMONIOUS Partners
COST Countries
32 Partners
HARMONIOUS Network

17. 17
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

18. 18
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

19. 19
The Home Page -
https://www.costharmonious.eu

20. 20
Twitter
https://twitter.com/COST_HARMONIOUS

21. 21
Facebook Harmonious-European-COST-
Action
228 followers on facebook
https://www.facebook.com/Harmonious-European-COST-
Action-485412205186817/

22. 22
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

23. 23
Field Activities for UAS-based DSM
(A) (B) (C)
WG meeting in Timisoara

24. 24
Working Group - Activities

25. 25
DSM accuracy in terms of planar and
vertical RMSE as a function of the GCPs
density
GCPs/Area (N./ha)
0 10 20 30 40 50 60 70 80
RMSEX,Y
,RMSEZ
(cm)
0
20
40
60
80
100
120
RMSE
X,Y
RMSE
Z
22
DSM accuracy in terms of planar and
vertical RMSE as a function of the GCPs
density
GCPs/Area (N./ha)
0 10 20 30 40 50 60 70 80
RMSEX,Y
,RMSEZ
(cm)
0
20
40
60
80
100
120
RMSE
X,Y
RMSE
Z
(Manfreda et al., R.S. 2018b)

26. 26
UAS data Collection
Fligth Fligth plan Level
Above the
Ground
(m)
Camera
Tilt
(degree)
Avg GSD
(cm/px )
Images
N.1 60 0° 1.9 276
N.2 60 0° 1.9 268
N.3 60 70° - 271
N.4 60 20° 2.0 273
N.5 60 0° 1.9 257
N.6 120 0° 3.3 85
(A) (B)
(D)(C)
(F)(E)

27. 27
Planar Coordinates - RMSEX,Y (m)
Flight N. 1 N.2 N. 3 N. 4 N. 5 N. 6
N. 1 4.47
N.2 2.39 2.03
N. 3 136.05 1497.25 X
N. 4 1.64 3.08 3835.20 7.75
N. 5 2.09 1.95 15042.56 8.05 7.15
N. 6 3.06 3.35 1750.11 8.63 6.94 19.70
Elevation - RMSEZ (m)
Flight N. 1 N.2 N. 3 N. 4 N. 5 N. 6
N. 1 82.90
N.2 81.18 78.72
N. 3 80.32 56.94 X
N. 4 79.21 76.94 15.51 75.02
N. 5 77.90 77.86 7.70 73.35 71.86
N. 6 78.79 75.48 20.25 72.85 70.27 59.75
Relative Elevation - RMSEZ (m)
Flight N. 1 N.2 N. 3 N. 4 N. 5 N. 6
N. 1 1.06
N.2 0.39 0.37
N. 3 3.74 19.55 X
N. 4 0.55 0.42 5.88 0.11
N. 5 0.39 0.25 8.00 0.47 0.26
N. 6 0.22 0.94 13.85 0.80 0.40 3.44
Planar and vertical - RMSE (m)
Flight N. 1 N.2 N. 3 N. 4 N. 5 N. 6
N. 1 4.59 Performances
N.2 2.42 2.06 High
N. 3 136.10 1497.38 X
N. 4 1.73 3.11 3835.20 7.75 Medium
N. 5 2.13 1.97 15042.56 8.06 7.15
N. 6 3.07 3.48 1750.16 8.67 6.95 20.00 Low
RMSE estimated on
16 GCPs for planar
coordinates, absolute
elevation, relative
elevation, and the
sum of the planar
and vertical error
obtained using
different images
taken from different
flight combinations

28. 28
RMSE of the 3D model
as a function of the
number of GCPs
adopted in the model
run. A) RMSE in planar
coordinates; B) RMSE
in relative elevation; C)
RMSE in X, Y, and Z
for the combination of
flights N.1 and N.4.
RMSE and Number of GCPs
1 2 3 4 5 6 7 8 9
RMSEX,Y
(m)
10-5
100
105
1 2 3 4 5 6 7 8 9
RMSEZ
(m)
10-2
10
0
102
Number of GCPs
1 2 3 4 5 6 7 8 9
RMSEX,Y,Z
(m)
10-2
100
102
<0.1m
<0.1m
<0.1m
(C)
(B)
(A)
(Manfreda et al., R.S. 2018b)

29. 29
RMSE and Number of GCPs
GCPs
1 2 3 4 5 6 7 8 9
RMSEX,Y
(m)
10
-4
10-2
100
102
GCPs
1 2 3 4 5 6 7 8 9
RMSEZ
(m)
10
-1
100
10
1
<0.1m
<0.1m
(A) (B)
Single Flight
Two Flights
Single Flight
Two Flights
GCPs
1 2 3 4 5 6 7 8 9
RMSEX,Y
(m)
10
-4
10-2
100
102
GCPs
1 2 3 4 5 6 7 8 9
RMSEZ
(m)
10
-1
100
10
1
<0.1m
<0.1m
(A) (B)
Single Flight
Two Flights
Single Flight
Two Flights
Comparison of results obtained changing the number
of GCPs and adopting a single flight or a two flights
dataset on the plane (A) and z-axes (B).
(Manfreda et al., R.S. 2018b)

30. 30
RMSE of the 3D model
as a function of the
mean distance between
GCPs obtained for the
flight N.2 (A, B) and for
the combination of
flights N.1 and N.4 (C,
D).
GCPs distribution
Mean Planar Distance (m)
0 50 100 150
RMSEX,Y
(m)
10-5
10
-4
10
-3
10-2
10
-1
100
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
Mean Vertical Distance (m)
0 5 10 15
RMSEZ
(m)
10-2
10-1
100
101
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
(A) (B)
Mean Planar Distance (m)
0 50 100 150
RMSEX,Y
(m)
10-4
10-3
10
-2
10-1
100
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
Mean Vertical Distance (m)
0 5 10 15
RMSEZ
(m)
10-2
10-1
100
101
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
(C) (D)
(Manfreda et al., R.S. 2018b)

31. 31
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) UAV mounted
Parrot Sequoia sensor at 50 m height (0.05 m); (B) a WorldView-3 image
(1.24 m); and (C) Planet CubeSat data (approx. 3 m), collected on the 13th,
29° and 27th March 2018, respectively
WG2
Vegetation Status

32. 32
UAS thermal survey over an Aglianico vineyard
in the Basilicata region (southern Italy)
WG2
Vegetation Status
(Manfreda et al., R.S. 2018a)

33. http://bestdroneforthejob.com/
WG3
Soil Moisture Monitoring

34. 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 that may be assumed coincident with the rooting
depth of vegetation (of the order of 60–150 cm).
This may allow the derivation of a function of the soil moisture in one
layer as a function of the other one.
Manfreda et al. (HESS - 2014)
s1(t)
s2(t)
First layer
Second layer
Zr2
Zr1
!! !! = !! + (!! !!!! − !!)!!!! !!!!!!!
+ 1 − !! !!! !! !! − !!!!

35. Sensitivity of SMAR’s parameters
The derived root zone soil moisture (SRZ) is plotted changing the soil water
loss coefficient (A), the depth of the second soil layer (B), and the soil
textures (C).
Manfreda et al. (HESS - 2014)

36. 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., 2016)

37. 37
Stream flow monitoring with UAS
Particle Tracking Velocimetry (PTV)
Image processing
WG4:
Stream Monitoring
Lagrangian method
(Tauro et al., 2016)

38. Particle Tracking
Particle detection Velocity vectors

39. Field Measurements

40. Surface Flow Velocity
Pixels
500 600 700 800 900 1000 1100
Pixels
400
500
600
700
800
900
[m/s]
0
0.5
1
1.5
2
2.5
3
Charcoal
y = 1.0849x + 0.0869
R² = 0.9
0
0.5
1
1.5
2
2.5
0 1 2 3
UAVderivedsurface
velocity(m/s)
Surface Velocity measure with
traditional techniques (m/s)

41. 41
Stream Flow Monitoring – Data Collection for
Benchmarking Optical Techniques
CASE STUDIES

42. 42
Original Video File Name: [River]_[Country][ddmmyearhhmmUTC].mov
Camera Model:
Platform used (gaugecam, drone, mobile, etc.):
Camera setting (autofocus, field of view, ISO, stabilization, …):
Video resolution (4000x2000, …)
Video frequency (Hz):
Presence of tracers and type:
Optional Info
Lumen:
Wind speed and orientation:
Case Study
River Name:
River Basin Drainage Area (km2
):
Cross-Section Coordinates (Lat, Long WGS84):
Flow regime (low, medium, high):
Ground-true availability (yes or not):
File Format (mov, avi, mp4, etc.):
Reference paper:
Processed Data
File Name of Processed Frames: [River]_[Country][ddmmyearhhmmUTC].zip
Number of frames:
Frame rate (Hz):
Pixel dimension:
Pre-processing actions (contrast correction, channel used, orthorectification,
stabilization, etc.):
Stream Flow Monitoring – Data Collection

43. Monitoring River Systems

44. 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)

45. Field Experience with UAS
Validation with
current meters

46. Conclusions
• 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.
46

47. 47
§ Perks, Hortobágyi, Le Coz, Maddock, Pearce, Tauro, Dal Sasso, Grimaldi,
Manfreda,(2018) Towards harmonization of image velocimetry techniques
for determining open-channel flow, Earth system science data (in
preparation).
§ Manfreda, Dvorak, Mullerova, Herban, Vuono, Juan Arranz Justel, Perks
(2018b) Accuracy Assessment on UAS-derived Digital Surface Models, Remote
Sensing, (under review).
§ 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, V. Pajuelo Madrigal, Mallinis, Ben Dor,
Helman, Estes, Ciraolo, Müllerová, Tauro, De Lima, De Lima, Frances,
Caylor, Kohv, Maltese, (2018a) On the Use of Unmanned Aerial Systems for
Environmental Monitoring, Remote Sensing.
Publication Produced