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Sélection de Projets des Zones Humides
2021
1. INTRODUCTION
TERRA Srl, fondée en 2000, est une entreprise active en Italie et à l’étranger dans les domaines de
l’écologie appliquée, de la gestion des terres, de l’hygiène environnementale et de l’évaluation et gestion
de processus complexes. L’entreprise s’adresse et collabore notamment avec des administrations
publiques, des associations non gouvernementales et des particuliers.
Dans son activité, TERRA Srl a toujours recherché de nouvelles solutions aux problèmes
environnementaux et territoriaux émergents, en adaptant constamment ses ressources professionnelles
et techniques et en développant de nouvelles méthodologies par elle-même.
Grâce à de nombreuses relations professionnelles et scientifiques, consolidées dans l’accomplissement
de plus de 1 000 missions, Terra Srl est en mesure de fournir des solutions à des problèmes territoriaux
et environnementaux complexes individuellement ou en collaboration avec des tiers.
Dans son activité de R et D, TERRA Srl a développé un innovant algorithme (Poseidon) par l’étude de
haute précision bathymétrie dérivée par satellite (SDB) ; l’algorithme est protégé de droits d’auteur
(T.E.R.R.A. S.r.l. – 2020).
TERRA Srl est inscrite au registre national de la recherche – Code CAR 53459PKG
Domaines d’activités • Études d’impact environnemental (EIE) • Évaluations environnementales stratégiques (EES) • Études d’incidences sur l’environnement (EIncE) • Analyse critique, analyse sectorielle et analyse du paysage (écologie du paysage) • Études d'impact environnemental (EIE) • Planification territoriale, urbaine et environnementale, du niveau local au niveau régional • Développement, conception, réalisation de travaux de génie de l’environnement • Conception, réhabilitation et gestion de systèmes naturels et anthropiques • Plans de caractérisation des sites pollués et définition de plans de réhabilitation • Planification et conception d’itinéraires cyclables et pédestres, de sentiers forestiers • Surveillance, analyse et modélisation des paramètres environnementaux • Gestion des processus participatifs et d’information • Planification et conception de plans énergétiques • Relevés topographiques, calculs numériques et modélisation tridimensionnelle du terrain
Principaux logiciels et équipements Afin de mener ses travaux de la meilleure façon possible, Terra maintient ses logiciels et équipements constamment mis à jour. À ce jour, Terra dispose des logiciels Cad&Pillar 2019, Primus, QGis, Rstudio, Gimp, Pacchetti Adobe, Acrobate et Office, File Maker. Terra dispose également d’une station totale Leica TCR705 pour les relevés topographiques.
1.1 Éthique et principes
Terra Srl a toujours appliqué des règles éthiques strictes dans la phase d’acceptation des missions
professionnelles, en rejetant toute forme d’exploitation non durable du territoire et toute activité ayant
un impact ou étant nuisible à la santé humaine, au paysage et à l’environnement. Ces choix, bien que
difficiles du point de vue du rendement économique des entreprises, ont permis à Terra de rester libre
de critiquer ouvertement et de combattre les choix de planification et les projets non partagés, aussi et
surtout en conseillant les administrations, comités et citoyens libres qui s’opposent à ces activités
nuisibles.
Ces choix ont garanti à Terra une cohérence constante dans la recherche de solutions de qualité
environnementale et sanitaire, en offrant à ses clients sélectionnés un motif de confiance et de valeur
ajoutée. Ce style a également permis de valoriser son réseau et son travail.
En 2019/2020, Terra Srl soutient financièrement Amnesty International pour l’interruption de
l’exploitation des personnes et des mineurs dans les mines de cobalt de la République démocratique
du Congo.
1.2 Structure de l’entreprise
Terra peut compter sur un réseau de collaborations largement utilisé dans les domaines de la
recherche, du conseil, du secteur public et des affaires.
C’EST POURQUOI, TERRA EST CAPABLE D’EXÉCUTER TOUT TYPE DE TRAVAIL DANS SON
PROPRE DOMAINE D’ACTIVITÉ.
ASSOCIÉS
• M. Marco Stevanin. Administrateur unique.
Membre de la Commission nationale EIE/EES du Ministère de l’environnement (2007-2008)
Membre de la Commission technique régionale de l’environnement (Commissione Tecnica
Regionale Ambiente – CtRA) de la région de Vénétie (depuis 2016)
• Dr. For. Marco Abordi. Responsable technique
• Ing. For. Gabriele Carraro
• Ing. For. Giacomo Gianola
• Ing. For. Pippo Gianoni
EMPLOYÉS
• Mme Cinzia Ciarallo
• M. Filippo Tonion. Recherche et développement
• M. Giovanni Orlando
• Mme Ilaria Gianolli
• Secr. Stefania Tonicello
Études D’incidences Sur L’environnement (Eince) Du Projet « Protection De La Côte De Bibione Dans La Municipalité De San Michele Al Taglaimento »
TYPOLOGIEProgrammation
CLIENTPublicLIEUBibioneDATEAoût 2012
DESCRIPTIONL’intervention en question porte sur la réalisation de travaux de défense pour protéger la côte de Bibione des ondes de tempête entre le phare de Punta Tagliamento (près de l’embouchure du fleuve Tagliamento) et la ville de Bibione. L’absence de barrières a conduit à un recul significatif du littoral et a provoqué la soustraction d’habitats naturels et agricoles. D’autre part, les valeurs environnementales et naturalistes de la zone ont également été reconnues au niveau européen, à tel point que la zone est incluse dans le site SIC IT3250033 « Laguna di Caorle– Foce del Tagliamentento » et sur le site ZPS IT3250040 « Foce del Tagliamento ».Toutes les interventions, en
phase d’E.Inc.E ont été jugées
compatibles avec le besoin de
protection des sites et des
habitats Natura2000, car
l’évaluation appropriée exclu la possibilité d’effets négatifs importants.
Figure 2. Relief botanique
Figure 1. Analyse historique
DOMAINES PROFESSIONNELS
PROGRAMMATION
Contrat de rivière d’AddaSourcesau lacdeCôme
TYPOLOGIEProgrammation
CLIENT
PublicLIEU
Côme
DATE
2014-2019
DESCRIPTION
Le but du travail était de définir
et mettre en œuvre un « Contrat
de rivière » pour le bassin
supérieur de la rivière Adda.
L’objectif de ce processus
participatif est la création d’une
gouvernance multi-niveaux pour
les zones relevant du bassin
versant concerné, afin
d’améliorer l’intégrité et la
fonctionnalité de ces
écosystèmes.
Terra a notamment coordonné
d’un point de vue technique et
scientifique les différents
tableaux thématiques, la
définition du projet
stratégique de longue période
et le programme d’action. Le
projet s’est terminé par la
signature d’un accord de
programmation négociée avec
la région de la Lombardie et
tous les sujets concernés.Figure 3. Rivière Adda
Figure 1. Logo Contrat de rivière d’Adda
Figure 2. Signature du contrat
DOMAINES PROFESSIONNELS
PROGRAMMATION
Conception et direction des travauxConsortiumBIMBassoPiave
TYPOLOGIE
Programmation, conception
et supervision des travaux
CLIENT
Public
LIEU
Bas Piave
DATE
2014-2020
DESCRIPTION
Terra a défini le projet de
sécurité du cours inférieur du
fleuve Piave à travers des
interventions visant à protéger
la végétation arboricole de la
plaine inondable.
Le projet a été développé en
collaboration avec les
ingénieurs civils de la région de
Vénétie, impliquant les
propriétaires privés des bois
présents dans les zones de
plaine inondable du cours
inférieur du fleuve Piave.
Le projet a été partagé avec
l’ensemble des acteurs locaux
afin de définir des directives
d’intervention respectueuses du
paysage et de la naturalité des
lieux. Figure 3. Piste cyclable du bas Piave
Figure 1. Anse du fleuve Piave
Figure 2. Fleuve Piave
DOMAINES PROFESSIONNELS
PROGRAMMATION
CONCEPTION
DIRECTION DES TRAVAUX
Contrat de zone humide du système lagunairede CaorleAccorddeprogrammationnégociée
TYPOLOGIEProgrammationCLIENTPublic-privéLIEUCaorle (VE)DATE2018-2019
DESCRIPTION
Le but du travail était celui de
définir et concrétiser, dans le
domaine du projet européen
Wetnet, un « Contrat
de zone humide » pour le
système lagunaire de Caorle. Le
but de ce processus participatif
est la création d’une
gouvernance à plusieurs niveaux
pour les zones humides de la
Méditerranée afin d’améliorer
l’intégrité et la fonctionnalité de
ces écosystèmes. Terra a
notamment coordonné d'un
point de vue technique
et scientifique les différents
tableaux thématiques, la
définition du projet
stratégique de longue période
et le programme d’action.
Le projet s’est terminé par la
signature d’un accord de
programmation négociée avec
la région de Vénétie et tous les
sujets concernés.Figure 3. Lagon de Caorle
Figure 1. Project Wetnet logo
Figure 2. Signature du contrat
DOMAINES PROFESSIONNELS
PROGRAMMATION
Analyse comparative du cycle de viededeuxsystèmesd’éliminationdes sédimentsdans le canal duport deCervia.
TYPOLOGIEAnalyse du cycle de vieCLIENTPublicLIEUCervia (RA)DATEJanvier 2020
DESCRIPTION
L’objectif du travail était de
produire une analyse
comparative du cycle de vie
(LCA) de deux systèmes contre
l’ensablement du port de Cervia.
Les deux systèmes considérés
sont les suivants :
-Usine innovante de dessablage
avec éjecteurs (LIFE15 ENV/IT
/000391 Marina Plan Plus) ;
- Opérations de dragage
traditionnelles.
Le studio Terra a rédigé la Life
Cycle Assessment (Analyse du
cycle de vie) conformément aux
normes ISO 14040 et ISO 14044,
en tenant compte à la fois des
impacts environnementaux et
socio-économiques.Figure 3. Analyse comparative des émissions de CO2
Figure 1. Emplacement de l'usine à l'embouchure du port de Cervia
Figure 2. Dessin du projet
0
40000
80000
120000
160000
200000
240000
Scenario 1
12 000 m3
Scenario 2
66 000 m3
Scenario 3
120 000 m3
KgCO2eqGWP100 des deux systèmes dans les trois
scénarios
Marina Plan Plus -
Renewable Energies
Marina Plan Plus -
Energy Mix
Marina Plan Plus -
Fossil Fuels
Dredging
DOMAINES PROFESSIONNELS
PROGRAMMATION
Recherche et développementTélédétection pour l’étude des phénomènes environnementaux et des impacts
associés
TYPOLOGIERecherche et développement
DESCRIPTION
Le studio Terra travaille
constamment sur le
développement de nouveaux
produits et services.
L'objectif est de proposer des
solutions innovantes dans
différents domaines de l’étude
combinées avec des techniques
traditionnelles. L’étude des
phénomènes environnementaux
par télédétection est un domaine
de recherche particulier du studio.
TERRA finance en effet une
formation doctorale à l’Université
de Padoue.
Cette activité de recherche
concerne le développement
d’algorithmes pour l’analyse de
données satellitaires avec des
techniques de machine et
d’apprentissage approfondi.
Jusqu’à présent, les principaux
domaines de recherche ont été les
suivants :
1)Bathymétrie par satellite ;
2)Estimation de la pollution
atmosphérique ;
3)Analyse de l’utilisation des sols ;
4)Production cartographique à
partir d’images satellites ;
5)Fusion de données radar et
optiques. Figure 3. Corrélation entre la profondeur prévue et la profondeur réelle – Random Forêt
Figure 1. Carte bathymétrique de Cesenatico –Algorithme de Poséidon
Figure 2. Exemple de réseau neuronal appliqué
DOMAINES PROFESSIONNELS
RECHERCHE ET DÉVELOPPEMENT
Annexe 1. Publication Internationale
2021
A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED
BATHYMETRY
F. Tonion 1 2, F. Pirotti 2,3,*, G. Faina4, D. Paltrinieri 4
1 T.E.R.R.A. S.r.l., Galleria Progresso 5, San Donà di Piave (VE), 30027, Italy – [email protected]
2 TESAF Department, University of Padua, Viale dell’Università, Legnaro (PD), 35020, Italy [email protected],
[email protected] 3 CIRGEO, University of Padua, Viale dell’Università, Legnaro (PD), 35020, Italy
4 COREMA S.r.l., San Giovanni in Persiceto (BO), 40017, Italy, [email protected], [email protected]
Commission III, WG III/10
KEY WORDS: Satellite Derived Bathymetry, Random Forest, Machine Learning, Data Fusion, Support Vector Machine
ABSTRACT:
Bathymetry in coastal environment plays a key role in understanding erosion dynamics and evolution along coasts. In the presented
investigation depth along the shore-line was estimated using different multispectral satellite data. Training and validation data derived
from a traditional bathymetric survey developed along transects in Cesenatico; measured data were collected with a single-beam sonar
returning centimetric precision. To limit spatial auto-correlation training and validation dataset were built choosing alternatively one
transect as training and another as validation. Each set was composed by a total of ~6000 points. . To estimate water depth two methods
were tested, Support Vector Machine (SVM) and Random Forest (RF). The RF method provided the higher accuracy with a root mean
square error value of 0.228 m and mean absolute error of 0.158 m, against values of 0.409 and 0.226 respectively for SVM. Results
show that application of machine learning methods to predict depth near shore can provide interesting results that can have practical
applications.
1. INTRODUCTION
The study of bathymetry in coastal environment is becoming
increasingly important because of the strategic importance of
these areas and their vulnerability to different pressure factors.
Coastal areas fragility derives in particular from the constant
pressure of intense anthropogenic activities such as urbanisation,
exploitation of natural resources, and climate change-induced
natural hazards (Paterson et al., 2011). Considering these aspects
bathymetry is particularly important to get a direct measure of the
magnitude of these phenomena.
Traditionally bathymetric surveys are conducted using different
high precision tools, such as single or multi beam echo sounders.
This way to collect information produces high precision
measures. On the other hand, high survey costs and difficulties
connected to large area measurement (time required, dependency
on weather, sea condition etc…) are the main limitation of this
approach to bathymetry.
In the last years the use of satellite to estimate water depth
became an important support tool to compensate the limitations
of traditional surveys. In fact, Satellite Derived Bathymetry
(SDB) provide an estimation of water depth, that can easily deal
with large areas. Moreover, SDB analysis can be repeated over
time. There are of course limitations related to water turbidity,
weather and satellite data availability.
About the kind of satellite products to be used there are various
scientific articles that show the utilization different kinds of
products, such as multispectral data and SAR. Spectral methods
use light absorption models to correlate depth. Lidar technology
also plays a key role, with bathymetric lidar being an existing
technology in the market (Pirotti, 2019). SAR applications use
* Corresponding author
linear dispersion relation of wave properties to estimate the water
depth (Pereira et al., 2019; Wiehle and Pleskachevsky, 2018).
From a literature analysis it is possible to observe also different
approaches to data elaboration and analysis. Some articles show
water depth estimation using some empirical models, such as
Stumpf’s or Jupp’s models (Danilo and Melgani, 2019; Deidda
et al., 2016; Stumpf et al., 2003; Tang et al., 2019). This approach
generally leads to high uncertainty on estimations. Another way
to analyse data is the utilization of physics-based models, with
the consideration of parameters like sediment size and wave
dynamics (Brando et al., 2009; Lyzenga et al., 2006). This kind
of solutions lead to relatively low uncertainty on predictions.
More recently the use of Machine Learning (ML) to estimate
SDB is providing promising results. In fact some investigations
showed good results in term of errors on water depth estimation,
i.e. Root Mean Square Error (RMSE) generally lower than 1m.
(Manessa et al., 2016; Mavraeidopoulos et al., 2019; Pike et al.,
2019; Sagawa et al., 2019).
The analysis of satellite data using a ML approach counts on a
large number of algorithms that allow to refine estimations
depending on particular aims or conditions. Dealing with satellite
data analysis two consolidated ML algorithms are:
• Support Vector Machines (SVM)
• Random Forest (RF)
SVM is a supervised non-parametric statistical learning
technique (Mountrakis et al., 2010). This method was originally
introduced to separate two classes by defining an hyperplane, that
is the linear decision function with maximal margin between the
vectors of the two classes (Cortes and Vapnik, 1995). The margin
is defined by the so called “support vectors”.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020 | © Authors 2020. CC BY 4.0 License.
565
SVM can be used booth for regression and classification
problems. The particular efficiency of this approach is due to the
possibility to transform data with different kernel functions
(linear, sigmoid, radial and polynomial).
The RF algorithm was introduced in 2001 (Breiman, 2001); this
model is based on an ensemble of decision trees (forest) that
grows through training towards best combinations. An ensemble
consist of a set of individual trained classifier (decision trees),
which are combined for classify new instances (Kulkarni, 2013).
Considering the promising results obtained by ML, this study
aims to evaluate different ML techniques performance in SDB
analysis. In particular this study aims to:
Evaluate the best ML algorithms among those considered
(SVM and RF)
Evaluate the potential of different multispectral satellite data
fusion in SDB analysis.
2. MATERIALS AND METHODS
2.1 Study Area
The study area is situated in Cesenatico (FC) in Italy. Cesenatico
is a small town on Adriatic costs that in the past years was
particularly affected by sand deposition and erosion phenomena.
These were particularly problematic because of the touristic
vocation of Cesenatico beaches. As evidence of the susceptibility
to this kind of problems it is possible to observe that along
Cesenatico coasts, there are a lot of artificial reefs, situated
approximately at 300-400 m of distance from shoreline; this kind
of structures were built in past years as defence against sand
erosion. Their efficiency is limited to small area, and causes
further erosion/deposition dynamics in other parts of the coast.
The study area is situated in the northern part of Cesenatico port
and it is extended approximately 1,78 km2 (Figure 1). The area
includes the final part of Cesenatico artificial reefs.
Figure 1. Area of analysis
2.2 Data collection
Water depth data were collected on 26 April 2018 with a
traditional bathymetric survey (range of depth 0m-5m). The
survey was developed along transects perpendicular to beach axis
(Figure 2). The transects length was approximately 500m –
700m. Along transects each point of measure was approximately
~1m distant from other measures (500-700 points of measure per
transect).
Figure 2. Points of water depth measure
Water depth measures were taken from low draught boat, with a
single-beam echo sounder. The instrument precision on water
depth measures was centimetric. Data collected were managed
by a dedicated software, which provided georeferenced output.
2.3 Satellite Data
The estimation of SDB was based on different platform
multispectral satellite data. In particular the following satellite
data were used:
• Landsat 8. Bands 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11. The spatial
resolution of Landsat 8 bands vary from 15m to 100m, while
spectral resolution range comprehend spectral reflectance from
visible to Short Wave Infrared (SWIR).
• Sentinel 2. Bands 2, 3, 4, 5, 6, 7, 8, 9, 10, 11and 12. Sentinel 2
data spatial resolution vary from 10m to 60m, while spectral
resolution range of selected bands vary from visible to SWIR.
• Planetscope. Bands 1, 2, 3 and 4. Planetscope data have a
spatial resolution of 3m and are collected at visible and Near
Infrared (NIR) wavelength.
Data from above mentioned satellite platforms were acquired on
the 26th of April 2018, in order to compare reflectance values with
measured water depth.
2.4 Data Processing
The processing phase concerns using satellite data as vector of
variables for training and fitting the ML model. Measured data
from bathymetric survey were used as training and validation of
ML models, without any kind of elaboration. Satellite data
processing phase was developed with a data fusion approach. In
particular all data from the three different satellite platforms were
kept in consideration. Dealing with different spatial resolution
and different grid products the first preliminary operation was the
resampling of all data to the highest spatial resolution grid, i.e. 3
m from the Planetscope images. Considering that the spectral
signature of shallow water shows greater reflectance values on
the green part of the visible area of the spectrum, decreasing
drastically over the red and infrared part, all visible bands of three
different satellite platforms were kept in consideration as
predictors.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020 | © Authors 2020. CC BY 4.0 License.
566
Reflectance values of some bands were then combined in order
increase predictors number. In particular some predictors were
created using well consolidated remote sensing indexes, basing
both on Sentinel 2 and Landsat 8 data.
The indexes calculated are:
- Normalized Difference Water Index (NDWI)
- Modification of Normalized Difference Water Index
(MNDWI)
- Automated Water Extraction Indexes (AWEI)
- Visible-Band Difference Vegetation Index (VDVI)
- Water Index (WI)
After that different other predictors were created by combining
mainly visible bands reflectance (such as normalized difference
ratio) of all different satellite data (Planetscope, Sentinel 2,
Landsat 8). The above described process generated vectors with
53 variables, i.e. corresponding raster maps of predictors that
were used for training the ML methods. The dataset was created
by extracting predictors variable values at each measured water
depth point.
The whole dataset was then divided in training set and validation
one. Due to the spatial distribution of the measured data points,
it was decided to choose alternatively one transect as training and
another as validation, as shown in the figure below (Figure 3). In
fact, it was noticed that classic resampling technique used for
dividing the dataset in training and validation (e.g. cross
validation, random split), leads to a high probability of choosing
neighbouring points. This led to an initial overestimation of the
models’ accuracy, due to the lack of spatial independence
between training and validation points. The split produced a
training set that comprehend 6727 records and a validation one
with 6585 records.
Figure 3. Training (yellow) and validation (red) transects
To estimate the performance of the SDB methods, two different
algorithms were compared: Support Vector Machine SVM) and
Random Forest (RF). The choice of the best configuration of
algorithm hyper-parameters was achieved using an iterative
procedure that tested against several combination.
At each iteration algorithms were trained on training dataset,
using different configurations, and then the estimation of water
depth was performed on validation one. At each iteration the
Root Mean Square Error (RMSE) was calculated, using the
following equation (1).
𝑅𝑀𝑆𝐸 = √∑ (𝑦𝑝𝑟𝑒𝑑−𝑦𝑜𝑏𝑠)2𝑁 (1)
where ypred is the predicted water depth, yobs is the measured
(observed) water depth and N is the number of record of
validation dataset. Finally, for each algorithm it was selected the
parameter configuration that minimized the RMSE. For the
selected configurations also Mean Absolute Error (MAE) was
computed, using the following equation (2).
𝑀𝐴𝐸 = ∑ |𝑦𝑝𝑟𝑒𝑑−𝑦𝑜𝑏𝑠|𝑁 (2)
where ypred,i is the predicted water depth, yobs is the observed
water depth and n is the number of points of validation dataset.
In the next section a specific description of parameter setting of
each of the algorithms compared is provided.
1.4.1. Support Vector Machine (SVM)
SVM setting procedures dealt mainly with kind of kernel and
scale factor. The parameter “Kernel” represents the kind of
transformation function that substitute the scalar product of
predictors. The main kinds of function are linear, radial,
polynomial and sigmoid.
During the model tuning it was observed that radial, polynomial
and sigmoid kernel reduced a lot the dimensions of dataset; this
reduction was due to these specific kernel functions that, dealing
with negative values, produced not real number (omitted by
algorithm as NaN). For this reason it was decided to use linear
kernel function. About the scale factor it is a parameter that
defines the variable to be scaled. It was observed that any changes
in this parameter didn’t produce any improvement in prediction. Basing on this result it was maintained the default value that is 1.
1.4.2. Random Forest (RF)
RF tuning procedures kept in consideration the parameters
“ntree” and “mtry”. The “ntree” parameters represent the number of decision trees built by the model. The “ntree” variation range considered was from 100 to 2000 trees. The “mtry” parameter represents the number of variables to be split during to build each
tree. The “mtry” variation range assumed was from 2 to 30.
3. RESULTS
The iterative procedure to identify the best configuration of
algorithm lead to the choice of the following parameters for RF:
“ntree” was set equal to 600 and “mtry” equal to 10. The next plot presents RMSE variation corresponding to different
parameter configuration.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020 | © Authors 2020. CC BY 4.0 License.
567
Figure 4. RMSE variations depending on mtry and ntree
The results of algorithm tuning phase showed that RF prediction
were characterized by lower RMSE values. The following table
(Table 1) shows a comparison of algorithms best performances.
Algorithm RMSE (m) MAE (m)
RF 0.228 0.158
SVM 0.409 0.226
Table 1. Comparison of ML algorithms results
The next figures show, for each algorithm, the scatter plot of
predicted and observed water depth, for all the points
comprehended in the validation dataset.
Figure 5. RF predicted-observed plot
Figure 6. SVM predicted-observed plot
In the following table (Table 2) a comparison of the two different
algorithms in term of coefficient of determination R2 is presented,
considering predicted depth and observed depth.
Algorithm R2
RF 0.97
SVM 0.89
Table 2. Comparison of different algorithms coefficient of
determination.
Following figures show different bathymetric map of the study
area using different algorithms.
Figure 7. Bathymetric map using SVM
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020 | © Authors 2020. CC BY 4.0 License.
568
Figure 8. Bathymetric map using RF
The following plots (Figure 9) present a comparison of three
representatives transect section using measured (observed) depth
and depth predicted by the two ML algorithms. The three
transects are taken from the ones used for validation, therefore
represent independent measures. Results in the top plot in Figure
9 show that water deeper than 4 m is overestimated, reasonably
water composition at that point was different from the one used
for training.
Figure 9. Depth values of three of the transects used for
validation.
4. DISCUSSION
The two tested methods, RF and SVM provided acceptable
results, with RF providing higher accuracy values in this study.
SVM algorithm RMSE value was equal to 0.409 m and the MAE
to 0.23m. Coefficient of determination, R2, calculated for
predicted depth and observed one, showed a good correlation
(R2= 0.89).
Dealing with negative variables it was not possible to adopt
different kernel function to transform data in order to improve
algorithm performances; in fact the use of other kernel functions
(polynomial, sigmoid and radial) lead to a strong reduction of
training and validation datasets (due to the omission of NaN
data).
In this study the best results were obtained with RF. This
algorithm, with the adopted configuration allows to reach
extremely content errors (RMSE=0.23m and MAE=0.16m).
Considering the spatial resolution of satellite products adopted
(highest spatial resolution 3m) the result is particularly good. In
fact it must be considered that inside each pixel the reflectance
value represents the reflectance of the average depth of that pixel,
with possible disturbance of turbidity, algae…ecc . For this
reason while comparing real punctual depth to the predicted pixel
depth it is reasonable that some differences remain. Punctual
depth in fact can for example give indication of local pits
underwater.
However, the exam of longitudinal profiles obtained by measured
data and predicted ones (Figure 9) shows that results are really
closed, especially RF one.
In general, it is worth noting that using the ML approach, training
must be provided for obtaining such good predictions, therefore
care must be taken if the trained model is to be used over a dataset
from different imagery and from different dates from those used
in training. As matter of fact the water turbidity and other factors
can be different, therefore making new predicted results not
useful. Nevertheless, a well-trained RF model can provide
distributed information using a few surveyed points. A scenario
can be one of making a fast survey with only a few transects, thus
decreasing time-in-the-field and therefore economic effort.
Another point to take care of is that all forms of disturbance from
clear water will decrease the accuracy and also cause spurious
errors. For example algal bloom will provide a very different
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020 | © Authors 2020. CC BY 4.0 License.
569
reflectivity value and reasonably underestimate depth (i.e. deep
water might be estimated as shallow). The same can happen with
dark materials (e.g. oil spill) that absorb light and thus show
shallow water as deep water. To avoid these errors,
circumstances have to be verified before taking results as final
and with the accuracy values reported in this paper.
Further future investigation in this direction will include a
definition of variable importance, and further analysis on the
implication of using fewer variables and fewer training points.
This solution can be particularly indicated if the aim of analysis
is the estimation of sand volume movements.
5. CONCLUSIONS
The study has compared three different ML algorithms
performances in predicting sea water depth. The approach to data
analysis was a data fusion one, because data from different
platform were kept in consideration together in training and
validation phases. Results show that RF, with data fusion
approach, gave particularly good results with extremely content
errors. In conclusion this study shows that ML can be an efficient
tool to predict shallow water depth. In this way SDB can support
traditional bathymetric surveys in order to get a more efficient
monitoring tools.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020 | © Authors 2020. CC BY 4.0 License.
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