Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments

Prof. Salvatore Manfreda
Full Professor of Hydrology and Advanced Monitoring
Department of Civil, Architectural and Environmental Engineering
University of Naples Federico II, Italy
Email: salvatore.manfreda@unina.it
Web: www.salvatoremanfreda.it
EO4Geo Series: Drones in Geographical
Applications
EO4Geo Series: Drones in Geographical Applications, 09 February 2023, 04:00 pm (CET)
Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments

Salvatore Manfreda - EO4Geo Series: Drones in Geographical Applications, 09 February 2023, 04:00 pm (CET)
Scope: improve ability to monitor river basin processes
Variable of interests: vegetation, soil, and river systems.
Objective: define integrated procedures to improve environmental monitoring
capacity using UAS.
Scale: from plot-scale to the river basin scale providing operational monitoring
tools.
Unmanned Aerial Systems for Environmental Monitoring

Environmental Monitoring
Up to 30cm Up to 1cm Up to 1cm
Comparable Scales
Few hectars Few m2
Global
Scale Gap

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

from 1990 up to 2022 (last access 1/05/2022)
(Manfreda et al., Remote Sensing 2018)
Articles extracted from ISI web of knowledge

COST Action Harmonious - www.costharmonious.eu
A network of scientists is currently cooperating within the framework of a COST
(European Cooperation in Science and Technology) Action named
“Harmonious”.
• The aim is to promote monitoring strategies, establish harmonized
monitoring practices, and transfer most recent advances on UAS
methodologies to others within a global network.
• HARMONIOUS involves 36 Countries
HARMONIOUS TEAM: Salvatore Manfreda, Brigitta Toth, David Helman, Giorgos Mallinis, Richard Lucas, Pauline Miller, Antonino
Maltese, Petr Dvořák, Sander Mücher, Giuseppe Ciraolo, Yijian Zeng, Zhongbo Su, Jana Müllerová, Nunzio Romano, Xurxo Gago,
Ruodan Zhuang, Goran Tmusic, Helge Aasen, Mike James, Gil Goncalves, Eyal Ben-Dor, Anna Brook, Maria Polinova, Jose Juan
Arranz, Janos Meszaros, Ruodan Zhuang, Kasper Johansen, Yoann Malbeteau, Matthew McCabe, Isabel Pedroso de Lima, Corine
Davis, Sorin Herban, Sophie Pearce, Robert Ljubicic, Salvador Peña-Haro, Matthew Perks, Flavia Tauro, Alonso Pizarro, Silvano F.
Dal Sasso, Dariia Strelnikova, Salvatore Grimaldi, Ian Maddock, Gernot Paulus, Jasna Plavšic, Dušan Prodanovic, Rafi Kent, Josef
Brůna, Martynas Bučas, Joan Estrany, Adrien Michez, Martin Mokroš, Maria Tsiafouli, João L. M. P. de Lima, Anette Eltner, László
Bertalan

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.
UAS data processing

Vegetation Status: comparison of Satellite, 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).

Soil Water Content Monitoring
(Su et al., Water 2020)
a) Soil moisture downscaling
workflow based on random
forest regression (RF);
b) The importance of land
surface features for the RF
model;
c) RF-based soil moisture
products at 15cm;
d) Comparison of the
downscaled soil moisture
with in-situ measurements.

(Dal Sasso et al, 2021)
Number of papers extracted
from the Scopus database
published between 1980
and 2020 using the
keywords: “river” and
“velocity” with “image
velocimetry” or “current
meter” or “radar”
Image Velocimetry vs Other Techniques

Image Velocimetry Techniques: Example of application

Tools and Techniques for Stabilising UAS imagery
(Ljubičić et al., 2021)

River Monitoring
Image Velocimetry
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).
(Manfreda and McCabe, Hydrolink 2019)

Data Collection for Benchmarking Optical Techniques
Article: (Perks et al., ESSD 2019)
Data: (Perks et al., ESSD 2019)
Image velocimetry data available for 13 case studies

UAS data Collection: How to improve DSM accuracy
(A) (B)
(D)
(C)
(F)
(E)
UAS-based tiled Model
N. GCPs
3 4 5 6 7 8 9
RMSE
X,Y
(m)
10-4
10-3
10-2
10
-1
100
N. GCPs
3 4 5 6 7 8 9
RMSE
Z
(m)
10-2
10-1
100
101
(B)
RMSE
X,Y
= 0.2cm RMSE
Z
= 10cm
RMSEZ
= 3.5cm
(A)
Single Flight
Combined Flights
Single Flight
Combined Flights
Fig. 2: 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).
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
Mean Planar Distance (m)
0 50 100 150
Relative
change
of
RMSE
X,Y
(%)
-200
-100
0
100
200
300
400
500
R
2
=0.15
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
Mean Vertical Distance (m)
0 5 10 15
Relative
change
of
RMSE
Z
(%)
-200
-100
0
100
200
300
R
2
=0.08
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
Mean Planar Distance (m)
0 50 100 150
Relative
change
of
RMSE
X,Y
(%)
-200
-100
0
100
200
300
400
500
600
R
2
=0.16
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
Mean Vertical Distance (m)
0 5 10 15
Relative
change
of
RMSE
Z
(%)
-200
-100
0
100
200
300
400
500
R
2
=0.00
3 GCP
4 GCP
5 GCP
6 GCP
7 GCP
8 GCP
9 GCP
(A)
(D)
(B)
(C)
Fig. 1: 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).
(Manfreda et al., Drones 2019)

Guidelines for UAS-based monitoring
(Tmušić et al., Remote Sensing 2020)

• output product
• quality parameters
• software and hardware
• experiment design
• environmental conditions
• internal (affected) parameters
Factors Affecting the output quality of UAS observation
NB: The arrow color indicates the type of
relationship: gray - direct dependence, orange -
inverse dependence.

Activities involved in UAS-based Mapping
(Tmušić et al., Remote Sensing 2020)

Workflow

Online Courses
Course Introduction Harmonious Action Advances on UAS Guidelines for UAS mapping
Soil Moisture Retrievals
Soil Mapping Vegetation Mapping River Monitoring
www.costharmonious.eu/video-lectures/

Operations Manual for UAS operations (OM)
• Section I - OPERATIONS MANUAL
• Section II - CONCEPT of OPERATIONS
• Section III - EMERGENGY RESPONSE PLAN (ERP)
www.costharmonious.eu/operations-manual
Operations Manual for UAS operations (OM) targeting environmental studies, accompanied
by an Emergency Response Plan (ERP) and an example of UAS operation description and risk
assessment according to JARUS SORA 2.0 (also known as ConOps).

HARMONIOUS BOOK
rebrand.ly/UAS

Final Remarks
• UAS-based remote sensing provides new advanced procedures to monitor vegetation,
soil and river systems.
• The detailed description of such variables will increase our capacity to describe water
resource availability and assist agricultural and ecosystem management.
• HARMONIOUS COST Action is supporting the definition of standardized protocols for
UAS-based applications to improve reliability of such technology.
• Most of our results will be collected in the book “Unmanned Aerial Systems for
Monitoring Soil, Vegetation, and Riverine Environments".

Questions?

Prof. Salvatore Manfreda – Università degli Studi di Napoli Federico II

Stay Connected with Us
Link
Link
Link
Link
@COST_HARMONIOUS

Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments

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