sélection de projets des zones humides

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


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


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


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


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


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.





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.





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





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





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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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