Ecosystem Mapping and Accounting in Italy Based on Copernicus and National Data through Integration of EAGLE and SEEA-EA Frameworks

Abstract

Developing appropriate tools to understand and protect ecosystems and the services they provide is of unprecedented importance. This work describes the activity performed by ISPRA for the mapping of the types of ecosystems and the evaluation of their related ecosystem services, to meet the needs of the “ecosystem extent account” and “ecosystem services physical account” activities envisaged by the SEEA-EA framework. A map of the types of ecosystems is proposed, obtained by integrating the main Copernicus data with the ISPRA National Land Consumption Map, according to the MAES (Mapping and Assessment of Ecosystems and their Services) classification system. The crop production and carbon stock values for 2018 were then calculated and aggregated with respect to each ecosystem. The ecosystem accounting was based on the land cover map produced by ISPRA integrating, according to an EAGLE compliant classification system, the same Copernicus and National input data used for mapping the types of ecosystems. The analysis shows the importance of an integrated reading of the main monitoring tools and the advantages in terms of compatibility and comparability, with a view to enhancing the potential of Copernicus land monitoring instruments also in the context of ecosystem accounting activities.

Full text

Citation: De Fioravante, P.; Strollo,
A.; Cavalli, A.; Cimini, A.; Smiraglia,
D.; Assennato, F.; Munafò, M.
Ecosystem Mapping and Accounting
in Italy Based on Copernicus and
National Data through Integration of
EAGLE and SEEA-EA Frameworks.
Land 2023,12, 286. https://doi.org/
10.3390/land12020286
Academic Editor: Richard Smardon
Received: 24 November 2022
Revised: 13 January 2023
Accepted: 15 January 2023
Published: 19 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
land
Article
Ecosystem Mapping and Accounting in Italy Based on
Copernicus and National Data through Integration of EAGLE
and SEEA-EA Frameworks
Paolo De Fioravante 1, Andrea Strollo 1, Alice Cavalli 2, Angela Cimini 3, * , Daniela Smiraglia 1,
Francesca Assennato 1and Michele Munafò1
1Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48,
00144 Rome, Italy
2Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia,
Via S. Camillo de Lellis, 01100 Viterbo, Italy
3Department of Architecture and Project, University of Rome La Sapienza, Piazza Borghese 9,
00186 Rome, Italy
*Correspondence: angela.cimini@uniroma1.it
Abstract:
Developing appropriate tools to understand and protect ecosystems and the services they
provide is of unprecedented importance. This work describes the activity performed by ISPRA for the
mapping of the types of ecosystems and the evaluation of their related ecosystem services, to meet
the needs of the “ecosystem extent account” and “ecosystem services physical account” activities
envisaged by the SEEA-EA framework. A map of the types of ecosystems is proposed, obtained by
integrating the main Copernicus data with the ISPRA National Land Consumption Map, according
to the MAES (Mapping and Assessment of Ecosystems and their Services) classification system.
The crop production and carbon stock values for 2018 were then calculated and aggregated with
respect to each ecosystem. The ecosystem accounting was based on the land cover map produced by
ISPRA integrating, according to an EAGLE compliant classification system, the same Copernicus and
National input data used for mapping the types of ecosystems. The analysis shows the importance of
an integrated reading of the main monitoring tools and the advantages in terms of compatibility and
comparability, with a view to enhancing the potential of Copernicus land monitoring instruments
also in the context of ecosystem accounting activities.
Keywords:
ecosystem accounting; Copernicus; land cover; SEEA-EA; MAES; EAGLE; ecosystem
services; carbon stock; crop production
1. Introduction
Ecosystems represent the base units to detect land changes and to assess environmen-
tal conditions, which enables the recognition of past and current ecological processes and
the related services supplied and the analysis of future scenarios [
1
–
3
]. The ecosystem’s
biotic and abiotic characteristics and state affect the energy flow, the nutrient cycle, and
the availability of resources, species, and habitat [
4
–
6
]. In this sense, an exhaustive, effec-
tive, and operational ecosystems distribution knowledge is a fundamental aspect in land
monitoring activities [7].
The whole legislative framework settled for by the EU in recent years refers to ecosys-
tem accounting, e.g., the Biodiversity Strategy for 2030 [
8
] and the related European Soil
Strategy [
9
], the Nature Restoration Law [
10
] adopted by the Commission in June 2022,
and the Healthy Soils Law that is under preparation. The United Nations Statistical Com-
mission, in its 52nd session of March 2021, attributed to the National Accounting System
and to the System of Environmental Economic Accounting (SEEA) the assessment of the
contribution of the environment to the economy and the impact of the economy on the
Land 2023,12, 286. https://doi.org/10.3390/land12020286 https://www.mdpi.com/journal/land

Land 2023,12, 286 2 of 22
environment. This is in order to provide data, indicators, and statistics to stakeholders, to
monitor these interactions, and to identify more sustainable development strategies. SEEA
Ecosystem Accounting (SEEA-EA) is now the reference framework under the proposal for
the amendment of Regulation (EU) No. 691/2011 on European environmental economic
accounts to include a new module on ecosystem accounts [
11
]. The proposed legal module
on ecosystem accounts has been adopted by the Commission in July 2022 and proposed
to the Council and Parliament for final approval. Relevant policies consider the SEEA-EA
framework, including the Nature Directives, the Marine Strategy Framework Directive,
the Common Agriculture Policy, the EU Forest Strategy, and the EU Pollinators Initia-
tive [
12
–
15
]. The SEEA-EA may also support the reporting under the 8th Environmental
Action Programme or the revised Regulation on Land Use Land-Use Change, and Forestry
(LULUCF) (EU) 2018/841 [
11
]; it can also be an example for further analysis models to
implement in international projects such as the Horizon 2020 project SERENA (Soil Ecosys-
tem seRvices and soil threats modElling aNd mApping), which is conducting an in-depth
analysis of models about soil threats, soil ecosystem services, and their bundles at the
European level [
16
]. The Natural Capital Accounting and Ecosystem Service Valuation
(NCAVES) project highlighted the potential of the SEEA-EA in supporting the calculation
and mainstreaming of many Aichi target indicators and Sustainable Development Goal
(SDG) indicators [
17
] and the link with other key international environmental conventions
and platforms, including the UNCCD, Ramsar, and IPBES [
18
]. The UN Committee of
Experts on Environmental-Economic Accounting (UNCEEA) conducted a “Broad Brush
Analysis of SDG Indicators” identifying 40 SEEA-relevant SDG indicators that are or can
be aligned with the SEEA [
19
]. In detail, the usefulness of the SEEA as a tool to mainstream
the environment and biodiversity into national planning processes is explicitly recognised
via SDG Indicator 15.9.1, “Progress towards national targets established in accordance with
Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011–2020”, which in part
B requires the “integration of biodiversity values into national accounting and reporting
systems, defined as implementation of the SEEA” [
20
]. The need to produce systematic
physical and monetary measurements of the flow of ecosystem services on a national scale
is also expressed at the national level by law 221/2015, which requires the development of
consolidated statistical and accounting systems for natural capital and the introduction of
indicators for assessing the impact of policies on it.
The SEEA-EA is a spatially based integrated framework conceived to create a coherent
and comprehensive view of ecosystems, allowing the organisation of biophysical data,
the measurement of ecosystem services, the tracking of changes in ecosystem assets, and
the linking of this information to economic aspects and other human activity. The SEEA-
EA aims to become the tool for ecosystem accounting globally by standardising data
production, thus making it accessible and comparable [
21
–
23
]. In detail, the following set
of accounts is defined [22]:
•
Ecosystem extent account, which organises information on the extent of different
ecosystem types (e.g., forests, wetlands, agricultural areas, marine areas) as a starting
point for ecosystem accounting.
•
Ecosystem condition account, which considers the ecological integrity of ecosys-
tems, evaluating the distance from a reference condition with respect to different
biophysical characteristics.
•
Ecosystem services physical and monetary flow account, which measures the supply
and use of ecosystem services by economic units.
•
Monetary ecosystem asset account, which evaluates stocks and changes in stocks
of ecosystem assets, based on the monetary valuation of ecosystem services at the
beginning and end of each accounting period.
•
Thematic accounts, which organise data on themes of specific policy relevance, such
as biodiversity, climate change, urban areas, and oceans.

Land 2023,12, 286 3 of 22
In order to provide a comprehensive measurement of Natural Capital and Ecosys-
tem Services, the European Commission initiative MAES (Mapping and Assessment of
Ecosystems and their Services) is a strong tool to support the effort to map and classify the
extent of ecosystems, providing the conceptual framework, methodologies, and indicators
to collect information on ecosystems and their services in Europe, in order to address policy
decisions. MAES responded to Action 5 of the European Biodiversity Strategy to 2020,
which lays the foundation for the development of a methodology for understanding the
condition of biodiversity and ecosystems and the pressures they are subject to. In response
to this task, since 2012, the MAES working group has developed a classification system
for ecosystems [
24
], and then deepened the issues related to monitoring and analysis of
ecosystem services [
2
]. The ecosystem classification method proposed in MAES is based
on the use of CLC classes aggregated on the basis of the relationships between land cover
(LC) and land use (LU) classes and EUNIS habitats in order to represent and collect in-
formation on large-scale ecosystems [
24
]. The result is a 12-class classification system,
which gathers 7 terrestrial ecosystems, a freshwater one, and 4 related to marine areas.
The ecosystem types proposed in MAES were used for the EU ecosystem extent accounts
within the INCA project (The official name of the INCA project is knowledge innovation
project on an Integrated system of Natural Capital and ecosystem services Accounting
for the European Union), which was used to test the System of Environmental-Economic
Accounting-Experimental Ecosystem Accounting (SEEA-EEA). The results were included
in the most recent version of the SEEA-EA handbook, adopted in 2021 [
25
]. In this context,
the INCA project has shown that by following the SEEA-EA guidelines, it is possible to
produce large amounts of data on ecosystem accounting, thus enabling consistent and
comparable information on ecosystems and ecosystem services at the European scale.
The MAES classification system has also been adopted by the Copernicus Land Moni-
toring Service (CLMS) Local Component (details are available at https://land.copernicus.
eu/local, accessed on 11 January 2023), which includes high spatial and thematic resolution
vector data relating to three main categories of areas that require specific and detailed mon-
itoring: the Riparian Zones data offer a mapping of riparian areas [
26
], the Coastal Zones
data map a buffer zone of 10 km from the coast line [
27
], and the Natura 2000 data map
protected areas [
28
]. The Copernicus CLMS also includes other relevant LC/LU products,
such as the CORINE Land Cover [
29
], Urban Atlas [
30
], and the new CLC Plus Backbone
data [
31
], and also numerous other geographic information on soils and related variables
(e.g., the state of the vegetation or the water cycle). One of the objectives of Copernicus is to
provide data organised according to criteria that guarantee comparability and interchange
between different EU countries. This need is particularly important in the context of LC/LU
monitoring, where products born in different contexts and for different needs have required
the definition of specific classification systems, which are often difficult to compare. In
order to coordinate data flows from a thematic point of view, within CLMS, the EAGLE
concept (EIONET Action Group on Land monitoring in Europe) was introduced. EAGLE
aims to define a conceptual methodology to describe land cover and land use information
from different classification systems by tracing them to the three categories: Land cover
components (LCC), Land use attributes (LUA), and Landscape characteristics (CH) [
32
].
This allows the understanding of overlaps and the possible conversions between different
classification systems, but also to define new ones. The EAGLE model aims to separate the
LC and LU components through data modelling systems applicable at different scales and
in different contexts, while maintaining compatibility with existing datasets.
Over the years, the Italian Institute for Environmental Protection and Research (ISPRA)
has introduced many different LC and LU products on a national scale for the Italian
territory, as much as possible in line with the EAGLE model, both through the classification
of Sentinel-1 and -2 data [
33
–
35
], and through the integration of existing data. In this sense,
a methodology was developed for the integration of Copernicus and national data, which
made it possible to produce a national scale EAGLE compliant land cover map starting
from the integration of CLMS Local (Coastal Zones, Riparian Zones, Urban Atlas, Natura

Land 2023,12, 286 4 of 22
2000) and Pan European (CORINE Land Cover, CLC Plus Backbone) LC/LU data with the
ISPRA National Land Consumption Map (LCM) [
36
]. This product overcomes some of the
main limitations of the CLC and MAES classification systems, such as the widespread use
of mixed LC/LU classes, and makes it possible to meet the needs of SEEA-EA Ecosystem
services’ physical accounting, becoming the basis for the assessment of ecosystem service
proposed by the annual ISPRA report on land consumption [37].
This research is a first attempt to map the MAES ecosystem types at a national scale,
in order to provide support data to the ecosystem extent accounting activity with respect to
the classes recognised internationally by SEEA-EA and considered at a national level for
the future accounting of ecosystem services. In this sense, a map of the types of ecosystems
was produced by integrating the main CLMS data and the LCM, while the ecosystem
services associated with crop production and carbon stocks in the soil were calculated and
expressed as a function of these types of ecosystems.
2. Materials and Methods
2.1. Overview
The following methodology describes the input data and the procedures adopted to
produce a map of the MAES types of ecosystems, useful for the activities of ecosystem extent
accounting in compliance with the SEEA-EA approach. The ecosystems thus identified
were associated with the values of ecosystem services relating to crop production and
carbon storage. The two ecosystem services are estimated with reference to the total stock,
starting from the LC map produced that integrated the main CLMS data with the LCM [
37
]
according to an EAGLE compliant classification system. The analysis was conducted for
the entire national territory, with reference to 2018 (Figure 1).
2.2. SEEA-EA Compliant Types of Ecosystems Map
To produce a map of the ecosystem typologies in compliance with the United Nations
SEEA-EA approach [22], the classification system of Table 1was defined.
The proposed classification system of the types of ecosystems refers to the MAES
classes, which are divided at the first classification level into terrestrial ecosystems, fresh-
water ecosystems, and marine ecosystems, and then further characterised at the second
classification level.
Terrestrial ecosystems are delineated starting from CORINE Land Cover classes and
are subdivided into:
•
Settlements and other artificial areas, i.e., urban areas where most of the human
population live and which also include significant areas for synanthropic species
associated with urban habitats. This class significantly affects other ecosystem types
and includes urban, industrial, commercial, and transport areas, green urban areas,
and mines, dumping, and construction sites.
•
Cropland, i.e., areas mainly dedicated to agricultural production, even with the pres-
ence of important natural areas.
•
Grassland, i.e., areas with a prevalence of herbaceous vegetation, which can include
managed pastures and natural and semi-natural pastures.
•
Forest and woodland include areas dominated by woody vegetation; they are very
important from the point of view of the provision of ecosystem services.
•
Heathland and shrub are dominated by moors, heathland, and sclerophyllous vegetation.
•
Sparsely vegetated ecosystems are all naturally unvegetated or sparsely vegetated
habitats, usually with extreme climatic conditions, such as bare rocks, glaciers, dunes,
beaches, and sand plains.
•
Inland wetlands include natural or modified mires, bogs, and fens, as well as peat ex-
traction sites. In these areas, water regulation and peat-related processes are associated
with specific species of animals and plants.

Land 2023,12, 286 5 of 22
Land 2023, 12, 286 5 of 23
Figure 1. Study area. The analysis was conducted for the entire Italian national territory.
2.2. SEEA-EA Compliant Types of Ecosystems Map
To produce a map of the ecosystem typologies in compliance with the United Nations
SEEA-EA approach [22], the classification system of Table 1 was defined.
Figure 1. Study area. The analysis was conducted for the entire Italian national territory.

Land 2023,12, 286 6 of 22
Table 1.
MAES ecosystem types adopted for the realisation of the ecosystem type map by integrating
Copernicus data with the LCM.
Ecosystem Types
I Classification Level II Classification Level
Terrestrial ecosystems
Settlements and other artificial areas
Cropland
Grassland (pastures, semi-natural and natural grasslands)
Forest and woodland
Heathland and shrub
Sparsely vegetated ecosystems
Inland wetlands
Freshwater ecosystems Rivers, canals, lakes, and reservoirs
Marine ecosystems Marine inlets and transitional waters
Freshwater ecosystems include permanent freshwater, inland water courses, and
water bodies.
For marine ecosystems, the only class considered was “Marine inlets and transitional
water”, which includes coastal wetlands, lagoons, estuaries, i.e., areas on the land–water
interface under the influence of tides and with salinity levels greater than 0.5
‰
. The other
types of marine ecosystems were not considered as they relate to areas outside the study
area; furthermore, they are not considered in CLMS data.
In detail, the map integrates the main local and Pan-European CLMS data and the
ISPRA LCM for the year 2018 (Table 2).
Table 2.
Input data used for the mapping of ecosystem types and to produce the land cover
map used for the ecosystem services assessment; LC= Land Cover classes, LU= Land Use classes,
CLMS= Copernicus Land Monitoring Service.
Name Data Type Classes MMU
National data Land Consumption map (ISPRA) Raster 17 (LC) Pixel 10 ×10 m
CLMS Pan-European Component CLC Plus Backbone Raster 12 (LC) Pixel 10 ×10 m
CORINE Land Cover Vector 44 (LC, LU) 25 ha (status)
5 ha (changes)
CLMS Local Component
Coastal Zones
Vector
55 (LC, LU) 0.5 ha
Natura 2000
Riparian Zones
Urban Atlas 27 (LC, LU) 0.25 ha (class 1)
1 ha (class 2–5)
The input data were reclassified according to ecosystems of Table 1and merged into
a single 10
×
10 m raster mosaic. The proposed typologies represent the basic units for
ecosystems state and services assessments from the local to national scale. This map aims
to provide a representation of the Italian territory with respect to the MAES classes in order
to be a useful support for the ecosystem extent accounting activity.
These ecosystem types have been associated with the values of the services relating
to agricultural productivity and organic carbon storage, calculated using the procedure
described in the following paragraphs.
2.3. EAGLE Compliant Land Cover for the Assessment of Ecosystem Services
The same input data described before (Table 2) were combined to produce a land cover
map for 2018 according to the methodology described in De Fioravante et al. [33].
The input data were converted to a 10
×
10 m resolution raster and reclassified accord-
ing to the classification system of Table 3, which is based on previous activities of the ISPRA
working group [
33
,
36
] and adopts a combination of land cover classes directly attributable

Land 2023,12, 286 7 of 22
to the EAGLE model Land Cover Components (LCC), integrated with appropriate Land
Characteristics (LCH) in order to increase the thematic detail of the classification system
and preserve the information content of the input data.
Table 3. Land cover map classification system.
Land Cover
I Level II Level III Level IV Level V Level
1
Abiotic
non-vegetated
surfaces
11 Artificial abiotic
12 Natural abiotic 121 Consolidated (bare rocks, cliffs)
122 Unconsolidated (beaches, dunes, sands)
2Biotic
vegetated
surfaces
21 Woody
vegetation
211 Trees
2111 Broad-leaved
21111 Preval. of oaks and other
evergreen broad-leaved
21112 Preval. of deciduous oaks
21113 Preval. of other native
broad-leaved
21114 Preval. of chestnut
21115 Preval. of beech
21116 Preval. of hygrophytes
21117 Preval. of exotic broad-leaved
21118 Preval. of olive trees
21119 Preval. of orchards
2112 Needle-leaved
21121 Preval. of Mediterranean pines
and cypresses
21122 Preval. of gold-Mediterranean
and mountain pines
21123 Preval. of spruce
21124 Preval. of larch and/or Swiss pine
21125 Preval. of needle-leaved exotics
212 Shrubs 2121 Vineyards
2122 Shrubland
22 Herbaceous
vegetation
221 Periodically 2211 Pastures
2212 Arable land
222 Permanent
3Water surfaces 31 Water bodies
32 Permanent snow and ice
4 Wetlands
At the first classification level, four macro-classes are defined (Abiotic non-vegetated
areas, Biotic vegetated areas, Water surfaces, and Wetlands). Abiotic non-vegetated areas
include any unvegetated surfaces and are subdivided into man-made artificial structures
(artificial abiotic surfaces) and natural material surfaces (natural abiotic surfaces, both
consolidated and unconsolidated). Biotic vegetated areas include any vegetated surfaces,
with or without anthropogenic influence. At the second classification level, woody and
herbaceous vegetation are distinguished. Woody vegetation is further subdivided at the
third, fourth, and fifth classification levels in different classes of broad-leaved trees, needle-
leaved trees, and shrubs, while for herbaceous vegetation, the classes of natural unmanaged
grassland, pastures, and arable land are distinguished. Water surfaces include natural or
artificial solid water (permanent ice) and liquid water (regardless of shape, position, salinity,
and origin). Wetlands are defined according to the definition provided by CORINE Land
Cover, and include inland wetlands (inland marshes and peat bogs) and coastal wetlands
(salt marshes, salines, and intertidal flats), while lagoons and estuaries are associated with
water bodies. The wetlands class was introduced at the first classification level to preserve
the information content of the input data, but it is not directly compatible with the EAGLE
model and will be better integrated in the classification system in future studies. For a
more detailed description of the classes, reference can be made to [
36
] and to the official
ISPRA [37] and EAGLE group [32] documentation.
The new Copernicus CLC Plus Backbone has allowed the distinguishing of different
typologies of LC and LU in the mixed classes. In detail, the woody component from
the shrub and herbaceous vegetation and the agricultural use from natural areas have
been identified. The reclassified data were then mosaicised, giving priority to the CLMS
Local data, which have higher geometric detail than the CLC. The latter was included in

Land 2023,12, 286 8 of 22
the areas not covered by Local data, while the fourth CLC classification level (available
for Italy) made it possible to detail the different broad-leaved and needle-leaved classes.
The map was used for the evaluation of ecosystem services described in the following
two paragraphs.
2.4. Crop Production
Crop production, understood as an ecosystem service, is the ecological contribution
to the growth of cultivated crops that can be harvested and used as raw material [
38
].
Crop production was calculated using the methodology defined by ISPRA [
37
,
39
], based
on data from the Italian National Institute of Statistics (ISTAT) agricultural census of
2013 [
40
]. These data are available at the municipality scale and provide information on
the area occupied by the different types of crops (expressed in hectares), and on their total
production (expressed in quintals (1 Quintal (q): 1 q = 100 kg; the unit is officially used for
data published by ISTAT, although it is not part of the International System of Units)), with
reference to both herbaceous and woody crops. Five classes of crops have been identified
(arable land, pastures, olive groves, vineyards, and orchards), and for each, the productivity
in terms of quintals produced per hectare occupied by the class has been assessed. The
productivity value was then traced back to the area of the single pixel (equal to 10
×
10 m)
for each province and attributed to all the pixels of that class on the 2018 land cover map.
The ecosystem service was calculated starting from the classes in Table 3, obtaining a map
with agricultural productivity values per pixel, which were then aggregated with respect
to the types of ecosystems. In this way, the agricultural production in 2018 associated with
each of the five crops classes for each ecosystem type was evaluated.
2.5. Carbon Storage
Carbon storage is a regulation service provided by terrestrial and marine ecosystems
thanks to their ability to fix greenhouse gases [
41
]. This service contributes to the regulation
of the climate at a global level and plays a fundamental role in the context of climate change
mitigation and adaptation strategies. The analysis of the carbon storage capacity referred
to the entire national territory, starting from the methodology reported by ISPRA [
37
] and
De Fioravante et al. [
36
], applied to the 2018 land cover map described above. The study
provided estimates of the stored carbon for each portion of the territory and each type of
land cover with reference to four main carbon pools [
42
], recognised and classified by the
Intergovernmental Panel on Climate Change [43]:
•
Above-Ground Biomass (AGB) includes all the tissues of plant organisms outside
the soil (such as stems, branches, leaves, seeds, etc.). The fraction of stored car-
bon is calculated starting from the growing stock volume multiplied by specific
multiplicative coefficients.
•
Below-Ground Biomass (BGB) includes the root system of plants. The volume is
calculated according to [
44
], considering the growing stock volume, the wood basic
density, the crown/roots ratio [44,45], and a biomass expansion factor.
•
The carbon content in the Dead Organic Substance (DOS) includes the necromass, the
woody plant residues, the litter, and the residues not yet decomposed.
•
The soil carbon considers organic and mineral layers up to a thickness of 30 cm. The
calculation is based on the 1 km resolution raster produced by CREA-ABP, CNR-Ibimet
as part of the Global Soil Partnership/FAO initiative [
46
], the data of the National
Inventory of Forests and Forest Carbon Tanks (INFC) [
47
], and other data from the
literature [33], assuming zero carbon stored by artificial areas.
The service was calculated starting from the classes of Table 3, obtaining a map of
carbon stock values per pixel, which were then aggregated with respect to the types of
ecosystems. In this way, it was possible to evaluate the carbon stock in 2018 for each
ecosystem type.

Land 2023,12, 286 9 of 22
3. Results
3.1. SEEA-EA Compliant Types of Ecosystems Map
Figure 2shows the map of the types of ecosystems for 2018, obtained from the reclassi-
fication of the CLMS and LCM data according to the MAES classes of Table 1.
Land 2023, 12, 286 10 of 23
Figure 2. Ecosystem type map with MAES compliant classification system (2018).
The spatial distribution of the ecosystem typologies of Figure 2 shows the following
results (Table 4).
Figure 2. Ecosystem type map with MAES compliant classification system (2018).

Land 2023,12, 286 10 of 22
The spatial distribution of the ecosystem typologies of Figure 2shows the following
results (Table 4).
Table 4. Surface statistics relating to the ecosystem typologies map for the Italian territory (2018).
Ecosystem Typologies
Region
Settlements and Other
Artificial Areas
Cropland
Grassland
Forest and Woodland
Heathland and Shrub
Sparsely Vegetated
Ecosystems
Inland Wetlands
Rivers, Canals, and
Lakes
Marine Inlets and
Transitional Waters
km2%km2%km2%km2%km2%km2%km2%km2%km2%
Piedmont 2376 7.7 8597 7.1 2469 8.9 9045 9.8 407 3.4 2231 18.0 1 0.7 275 9.2 0 0.0
Aosta Valley 96 0.3 99 0.1 342 1.2 1135 1.2 101 0.8 1476 11.9 1 0.7 13 0.4 0 0.0
Lombardy 3989 13.0 8569 7.1 2445 8.9 6375 6.9 253 2.1 1443 11.7 28 15.6 776 25.9 0 0.0
Trentino–Alto Adige 589 1.9 747 0.6 2120 7.7 7138 7.7 377 3.1 2530 20.4 1 0.7 102 3.4 0 0.0
Veneto 3005 9.8 8038 6.6 1276 4.6 4101 4.4 268 2.2 561 4.5 9 5.1 397 13.2 682 44.6
Friuli–Venezia Giulia 902 2.9 2367 2.0 392 1.4 3371 3.6 212 1.7 457 3.7 2 1.3 67 2.3 148 9.7
Liguria 600 2.0 436 0.4 385 1.4 3767 4.1 143 1.2 67 0.5 0 0.2 20 0.7 1 0.1
Emilia–Romagna 2782 9.1 12,125 10.0 1085 3.9 5668 6.1 155 1.3 251 2.0 45 25.4 219 7.3 171 11.2
Tuscany 2024 6.6 7983 6.6 1410 5.1 10,868 11.8 345 2.8 163 1.3 35 19.9 118 3.9 42 2.7
Umbria 664 2.2 3396 2.8 614 2.2 3544 3.8 32 0.3 47 0.4 6 3.1 152 5.1 0 0.0
Marche 896 2.9 4937 4.1 617 2.2 2702 2.9 40 0.3 106 0.9 0 0.1 26 0.9 1 0.1
Latium 2426 7.9 6852 5.6 1520 5.5 5498 5.9 239 2.0 384 3.1 6 3.1 261 8.7 16 1.1
Abruzzo 782 2.5 3585 3.0 1872 6.8 3844 4.2 258 2.1 411 3.3 3 2.0 39 1.3 1 0.1
Molise 266 0.9 2103 1.7 486 1.8 1452 1.6 84 0.7 29 0.2 1 0.5 20 0.7 1 0.1
Campania 2001 6.5 5599 4.6 920 3.3 4490 4.9 352 2.9 172 1.4 5 2.6 56 1.9 5 0.3
Apulia 2120 6.9 13,720 11.3 1409 5.1 1401 1.5 381 3.1 56 0.5 7 3.7 38 1.3 224 14.6
Basilicata 477 1.6 4372 3.6 1346 4.9 3273 3.5 260 2.1 202 1.6 1 0.6 59 2.0 2 0.1
Calabria 1084 3.5 5359 4.4 1091 3.9 6414 6.9 732 6.0 316 2.6 2 1.0 74 2.5 11 0.7
Sicily 2368 7.7 14,261 11.8 3069 11.1 3064 3.3 2007 16.5 802 6.5 13 7.5 104 3.5 31 2.0
Sardinia 1265 4.1 8214 6.8 2755 10.0 5326 5.8 5505 45.3 669 5.4 11 6.2 179 6.0 194 12.7
Italy 30,712 100.0 121,360 100.0 27,623 100.0 92,479 100.0 12,151 100.0 12,372 100.0 177 100.0 2996 100.0 1530 100.0
From the analysis of Figure 2and Table 4, a prevalence of the “Cropland” class emerges;
it occupies more than 40% of the national territory, concentrating in Sicily, Apulia, and
Emilia–Romagna, where one third of the class falls. Additionally important is the presence
of the “Forest and woodland” class, which occupies approximately 30% of the national
territory, with a prevalence in Tuscany, Piedmont, and Trentino–Alto Adige (more than
a quarter of the surface occupied by this class falls in these three regions). The “Sparsely
vegetated ecosystems” are mainly present in the alpine regions, while the “Heathland and
shrub” typologies fall in the two major islands. Almost half of the coastal ecosystems are
concentrated in Veneto and Friuli–Venezia Giulia, while more than 60% of those of the
“Inland Wetlands” are in Tuscany, Emilia–Romagna, and Lombardy.
3.2. Assessment of Ecosystem Services through an EAGLE Compliant Land Cover Map
The 2018 land cover map used for the assessment of the two ecosystem services is
shown in Figure 3.
The map refers to the EAGLE compliant classification system of Table 3, while the
composition of the land cover (Figure 4) shows a prevalence of forest areas, which occupy
approximately one third of the national territory (of which more than 80% consists of
broad-leaved trees) and arable land, which covers 30% of the national territory. Permanent
herbaceous vegetation and areas with sparse or no vegetation prevail in the high-altitude
alpine areas, while olive groves and orchards occupy approximately 5% of the national
territory and are particularly present in the south.

Land 2023,12, 286 11 of 22
Land 2023, 12, 286 12 of 23
altitude alpine areas, while olive groves and orchards occupy approximately 5% of the
national territory and are particularly present in the south.
Figure 3. Land cover map with EAGLE compliant classification system (2018).
Figure 3. Land cover map with EAGLE compliant classification system (2018).

Land 2023,12, 286 12 of 22
Land 2023, 12, 286 13 of 23
Figure 4. Surface statistics relating to the classes of the 2018 land cover map for the Italian territory.
The assessment of ecosystem services provided the results displayed in Figure 5 for
organic carbon stocks and Figure 6 for agricultural productivity, with both referring to
2018.
Figure 5. Carbon stock (2018).
Figure 4. Surface statistics relating to the classes of the 2018 land cover map for the Italian territory.
The assessment of ecosystem services provided the results displayed in Figure 5for
organic carbon stocks and Figure 6for agricultural productivity, with both referring to 2018.
Land 2023, 12, 286 13 of 23
Figure 4. Surface statistics relating to the classes of the 2018 land cover map for the Italian territory.
The assessment of ecosystem services provided the results displayed in Figure 5 for
organic carbon stocks and Figure 6 for agricultural productivity, with both referring to
2018.
Figure 5. Carbon stock (2018).
Figure 5. Carbon stock (2018).

Land 2023,12, 286 13 of 22
Land 2023, 12, 286 14 of 23
The highest carbon stock values are recorded in mountainous areas with a high pres-
ence of forest cover, in particular in the Central and Eastern Alps and in the southern
Apennines. In detail, the maximum value is recorded in Trentino–Alto Adige (Figure 7),
due to the significant presence of needle-leaved trees, and in Piedmont, followed by Tus-
cany, which is the region with the greatest extension of forest cover.
Figure 6. Crop production (2018).
Agricultural productivity varies as a function of the productivity values considered
for the different territories; it shows a maximum in the Po Valley and in the hinterland of
Naples (Figure 6). The maximum values are relative to Emilia–Romagna, followed by
Apulia and Lombardy. Overall, productivity exceeds 10 billion quintals in 6 of the 20 re-
gions (Figure 7).
Figure 6. Crop production (2018).
The highest carbon stock values are recorded in mountainous areas with a high
presence of forest cover, in particular in the Central and Eastern Alps and in the southern
Apennines. In detail, the maximum value is recorded in Trentino–Alto Adige (Figure 7), due

Land 2023,12, 286 14 of 22
to the significant presence of needle-leaved trees, and in Piedmont, followed by Tuscany,
which is the region with the greatest extension of forest cover.
Land 2023, 12, 286 15 of 23
Figure 7. National and regional values of crop production and carbon stock (2018).
3.3. Ecosystem Services Provided by the Types of Ecosystems
The results of the ecosystem services assessment refer to the single pixel of 10 × 10 m
(Figures 5 and 6). Starting with the spatialised data, the values were aggregated with re-
spect to the types of ecosystems, allowing the results that are shown in Figures 8 and 9,
which respectively refer to carbon stocks and crop production by ecosystem, both for 2018.
Figure 8 shows a concentration of carbon stocks in the ecosystem type of “Forest and
woodland”, where 58% of the total carbon stock is concentrated, followed by “Croplands”
and “Grasslands”. The remaining classes have negligible values, due to the scarce pres-
ence of vegetation.
Figure 7. National and regional values of crop production and carbon stock (2018).
Agricultural productivity varies as a function of the productivity values considered
for the different territories; it shows a maximum in the Po Valley and in the hinterland
of Naples (Figure 6). The maximum values are relative to Emilia–Romagna, followed
by Apulia and Lombardy. Overall, productivity exceeds 10 billion quintals in 6 of the
20 regions (Figure 7).
3.3. Ecosystem Services Provided by the Types of Ecosystems
The results of the ecosystem services assessment refer to the single pixel of
10 ×10 m
(Figures 5and 6). Starting with the spatialised data, the values were aggregated with
respect to the types of ecosystems, allowing the results that are shown in Figures 8and 9,
which respectively refer to carbon stocks and crop production by ecosystem, both for 2018.

Land 2023,12, 286 15 of 22
Land 2023, 12, 286 16 of 23
Figure 8. Carbon stock with respect to the types of ecosystems (2018).
More than 99% of the crop production is concentrated in the “Cropland” and “Grass-
land” classes, while marginal values can be found in “Forest and woodland” and
“Sparsely vegetated ecosystems”.
Figure 9. Crop production with respect to the types of ecosystems (2018).
4. Discussion
The approach adopted by SEEA-EA is increasingly a reference tool in the manage-
ment and production of environmental data connected with the ecosystem assets and the
services they provide, also allowing the analysis of the connections with economic aspects
and with other human activities. In Europe, this process is rapidly evolving. Indeed, many
countries have started to identify ecosystems’ extent and condition, and assess biodiver-
sity, ecosystems, and ecosystem services at the national scale [48,49]. In some cases, these
activities are aimed at improving the degree of thematic detail in ecosystem mapping,
supporting the achievement of EU legislative frameworks [50,51]. The assessments
Figure 8. Carbon stock with respect to the types of ecosystems (2018).
Land 2023, 12, 286 16 of 23
Figure 8. Carbon stock with respect to the types of ecosystems (2018).
More than 99% of the crop production is concentrated in the “Cropland” and “Grass-
land” classes, while marginal values can be found in “Forest and woodland” and
“Sparsely vegetated ecosystems”.
Figure 9. Crop production with respect to the types of ecosystems (2018).
4. Discussion
The approach adopted by SEEA-EA is increasingly a reference tool in the manage-
ment and production of environmental data connected with the ecosystem assets and the
services they provide, also allowing the analysis of the connections with economic aspects
and with other human activities. In Europe, this process is rapidly evolving. Indeed, many
countries have started to identify ecosystems’ extent and condition, and assess biodiver-
sity, ecosystems, and ecosystem services at the national scale [48,49]. In some cases, these
activities are aimed at improving the degree of thematic detail in ecosystem mapping,
supporting the achievement of EU legislative frameworks [50,51]. The assessments
Figure 9. Crop production with respect to the types of ecosystems (2018).
Figure 8shows a concentration of carbon stocks in the ecosystem type of “Forest and
woodland”, where 58% of the total carbon stock is concentrated, followed by “Croplands”
and “Grasslands”. The remaining classes have negligible values, due to the scarce presence
of vegetation.
More than 99% of the crop production is concentrated in the “Cropland” and “Grass-
land” classes, while marginal values can be found in “Forest and woodland” and “Sparsely
vegetated ecosystems”.
4. Discussion
The approach adopted by SEEA-EA is increasingly a reference tool in the management
and production of environmental data connected with the ecosystem assets and the services
they provide, also allowing the analysis of the connections with economic aspects and
with other human activities. In Europe, this process is rapidly evolving. Indeed, many

Land 2023,12, 286 16 of 22
countries have started to identify ecosystems’ extent and condition, and assess biodiversity,
ecosystems, and ecosystem services at the national scale [
48
,
49
]. In some cases, these
activities are aimed at improving the degree of thematic detail in ecosystem mapping,
supporting the achievement of EU legislative frameworks [
50
,
51
]. The assessments require
spatially explicit data and information to identify ecosystems and to delimit the LC/LU
classes to be considered for the calculation of ecosystem services. Delimiting the ecosystem
extent is the first of the five core accounts of SEEA-EA, with the approach defined by MAES
and adopted by the UN to perform it based on a classification of the types of ecosystems
according to classes derived from CLC. In recent years, several LC/LU CLMS data have
been introduced based on the MAES classification system and referring to critical areas
for ecosystem conservation and protection, such as Riparian Zones, Coastal Zones, and
Natura 2000. This research analyses and elaborates on these data, synthesising them with
the ISPRA LCM in a semantically consistent representation of the territory in terms of types
of ecosystems, which can be a useful basis for conducting further studies, primarily the
activity of the ecosystem extent account. The map constitutes a reference for the aggregation
of the results of the physical or economic assessment of ecosystem services, informing
about the relevance of each ecosystem in the provision of a given service. It also lends
itself to further refinements, for example, through the introduction of geological or climatic
data, or additional information for the ecosystem condition accounts, which record the
condition of specific characteristics of ecosystems at specific points in time. In this sense,
the map of the types of ecosystems is also suitable for conducting diachronic analysis
for evaluating the changes that occurred in a given period within an ecosystem, and in
its ability to provide ecosystem services or assessing trade-offs and synergies between
ecosystem services [
44
–
46
,
52
–
54
]. Actually, one of the first future research developments
will concern the calculation of the variation in the provision of ecosystem services associated
with the LC/LU changes that occurred between 2012 and 2018, to be evaluated starting
from the revised version of the ISPRA LC/LU map for 2012.
However, the map of the ecosystem typologies needs to be accompanied by more
detailed spatial data for the application of estimation models for the ecosystem services
physical flow account. In fact, the methodologies for calculating agricultural productivity
and carbon stock described above require a detailed knowledge of the shape and composi-
tion of agricultural and natural areas, allowing also to distinguish different types of tree
cover or periodic and permanent crops. The ISPRA EAGLE compliant land cover map
obtained integrating CLMS data with the LCM is conceived with the aim of maximising
the description of the territory from a thematic point of view, distinguishing trees from
herbaceous vegetation and shrubs in mixed areas classified as “Heterogeneous agricultural
areas” in the CLC and MAES input data, also considering the presence of agricultural
activities or natural areas; this allows for the estimation of ecosystem services over a large
area with a higher geometric detail and a better thematic characterisation of the territory
compared to CLC.
The fact that the land cover map derives from the same input data used for the
production of the map of the ecosystem typologies constitutes an added value for the
ecosystem services accounting, since the two products are coherent from a geometric point
of view and their integrated reading makes it possible to understand the composition of
the land cover within a certain ecosystem or, vice versa, to deepen the ecosystem functions
of the different portions of the territory pertaining to a given land cover class. In fact,
comparing the two products, the map of the ecosystem typologies shows a composition
of the territory in line with that found by observing the ISPRA land cover map, with a
prevalence of “Cropland” in the lowland areas and of “Forest and woodland” in the alpine
and Apennine areas. The shrubs are mainly on the islands, while sparse or herbaceous
vegetation prevails at high altitudes. Most of the carbon stocks fall in areas classified as
“Forest and woodland”, while almost all agricultural productivity falls in the “Croplands”;
however, in the two ecosystems, there are not only the LC/LU classes of (respectively)
trees and agricultural areas. The example of Figure 10 shows an agricultural area with a

Land 2023,12, 286 17 of 22
patch of natural vegetation in the central part (Figure 10a), which CLC classifies as 243
“Land principally occupied by agriculture, with significant areas of natural vegetation”
(Figure 10b). By tracing the CLC class to the corresponding MAES ecosystem typology,
the area falls into the “Cropland” typology (Figure 10d), while in the LC/LU map, the use
of the CLC Backbone data allowed the disambiguation of the natural woody vegetation
component from the agricultural component.
Land 2023, 12, 286 18 of 23
Backbone data allowed the disambiguation of the natural woody vegetation component
from the agricultural component.
In this sense, the overall values of ecosystem services obtained for each ecosystem (in
this case, cropland) should be understood as the synthesis of several different contribu-
tions, deriving from the presence of different classes of land cover and land use in each
type of ecosystem. Figure 11 shows that the “Forest and woodland” ecosystem type also
includes areas with permanent herbaceous land cover. In other cases, areas classified in
the LC map as arable land or permanent crops, but also areas covered by arboreal vegeta-
tion, both broad-leaved and needle-leaved, fall into the “Cropland” typology.
Figure 10.
Examples of land cover and land use classes within the types of ecosystems. In the
example there is a patch of natural vegetation surrounded by croplands (a). The area is classified as
243 "Land mainly occupied by agriculture, with significant areas of natural vegetation" by CLC (
b
) and
corresponds to the "Cropland" MAES ecosystem typology (
d
), while in the land cover map the natural
woody vegetation component the from the agricultural component (c) has been disambiguated.

Land 2023,12, 286 18 of 22
In this sense, the overall values of ecosystem services obtained for each ecosystem (in
this case, cropland) should be understood as the synthesis of several different contributions,
deriving from the presence of different classes of land cover and land use in each type of
ecosystem. Figure 11 shows that the “Forest and woodland” ecosystem type also includes
areas with permanent herbaceous land cover. In other cases, areas classified in the LC map
as arable land or permanent crops, but also areas covered by arboreal vegetation, both
broad-leaved and needle-leaved, fall into the “Cropland” typology.
Land 2023, 12, 286 19 of 23
Figure 10. Examples of land cover and land use classes within the types of ecosystems. In the ex-
ample there is a patch of natural vegetation surrounded by croplands (a). The area is classified as
243 "Land mainly occupied by agriculture, with significant areas of natural vegetation" by CLC (b)
and corresponds to the "Cropland" MAES ecosystem typology (d), while in the land cover map the
natural woody vegetation component the from the agricultural component (c) has been disambig-
uated.
As in many other studies on the mapping of ecosystem types, the proposed method-
ology uses the CLC as reference data, trying to increase its geometric and thematic detail.
Some mapping experiences conducted on a national [55] and regional [47,56] scale exploit
classifications of satellite data to increase the detail of the CLC, while in other cases, the
improvement is conducted on a national scale by photointerpretation with the help of
ancillary data [50] or using national datasets [57,58].
In this research, to increase the detail in the description of the ecosystems, data avail-
able for the entire European territory were used (the CLMS Local data), making the meth-
odology also applicable to medium-scale studies in other contexts, generating homogene-
ous and comparable data (the LCM can be replaced by Copernicus HRL Imperviousness).
Furthermore, the application of the methodology is not time consuming, as it does not
require significant pre-processing of the input data or intense photo-interpretation activi-
ties. The areas not covered by CLMS Local data have a lower spatial detail; however, the
use of CLC Plus Backbone data still allows improvement of the description of the territory
in the mixed CLC classes, disambiguating the LC classes.
Figure 11. Flows of land cover and land use classes within the types of ecosystems.
Figure 11. Flows of land cover and land use classes within the types of ecosystems.
As in many other studies on the mapping of ecosystem types, the proposed method-
ology uses the CLC as reference data, trying to increase its geometric and thematic detail.
Some mapping experiences conducted on a national [
55
] and regional [
47
,
56
] scale exploit
classifications of satellite data to increase the detail of the CLC, while in other cases, the
improvement is conducted on a national scale by photointerpretation with the help of
ancillary data [50] or using national datasets [57,58].
In this research, to increase the detail in the description of the ecosystems, data
available for the entire European territory were used (the CLMS Local data), making the
methodology also applicable to medium-scale studies in other contexts, generating homoge-
neous and comparable data (the LCM can be replaced by Copernicus HRL Imperviousness).
Furthermore, the application of the methodology is not time consuming, as it does not
require significant pre-processing of the input data or intense photo-interpretation activities.
The areas not covered by CLMS Local data have a lower spatial detail; however, the use of

Land 2023,12, 286 19 of 22
CLC Plus Backbone data still allows improvement of the description of the territory in the
mixed CLC classes, disambiguating the LC classes.
5. Conclusions
This study provides insights on the integration between CLMS-derived EAGLE compli-
ant LC/LU data and the UN approach to ecosystems classification, to supply the SEEA-EA
framework for the ecosystem accounting. The five SEEA-EA core accounts integrate the
main available and forthcoming data about the ecosystem assets extent, condition, and
value at multiple spatial scales into a standardised, robust, and modular framework, also
indicating data and knowledge gaps to be filled for a more comprehensive assessment;
actually, available data often do not ensure adequate spatial and/or temporal consistency,
conditioning the effectiveness of the assessment. This work focuses on ecosystem extent
accounting and ecosystem services physical accounting, which require spatial LC/LU data
with good thematic detail and broad coverage. The Copernicus Land Monitoring Service
framework plays a fundamental role in this area, e.g., CORINE Land Cover (CLC) is one
of the most suitable datasets currently used for Ecosystem accounting [
24
], acting as a
proxy of ecosystem types for reporting purposes, although for a detailed assessment of
ecosystem condition and ecosystem services physical accounting, more accurate data are
needed. In this research, the CLC was integrated with the CLMS Local data and the ISPRA
LCM, providing a land cover map and a map of ecosystem typologies that represent the
territory in more detail and which satisfy the following requirements:
-
They are in line with the EAGLE (the land cover map) and MAES (the ecosystem
typology map) standards in terms of classification systems;
-
They are comparable and compatible with each other from a geometric and thematic
point of view;
-
They are suitable for conducting ecosystem extent accounting (the land cover map)
and ecosystem services physical accounting (the type of ecosystem map).
The approach adopted by SEEA-EA is increasingly a reference tool in the management
and production of environmental data connected with the ecosystem assets and the services
they provide, assuming an important role in directives, reporting activities, international
projects, international conventions, and platforms such as the UNCCD, Ramsar, and IPBES,
and supporting the calculation and mainstreaming of Aichi indicators and SDG indicators.
On the other hand, the fact that there are currently few applications of the SEEA-EA
methodology [
11
] makes this field challenging and open to developments, especially for
the definition and identification of suitable input data. This research is a first attempt
to apply the SEEA-EA methodology to the Italian territory, starting from the currently
available data and proposing an approach in line with the UN and EAGLE standards. On
the one hand, this can be replicated in other EU territories with CLMS data availability
and on the other hand, it has the potential for development, both in the integration of
other SEEA-EA account activities (ecosystem condition account, monetary flow account,
monetary ecosystem asset account, thematic accounts) and in the refinement of those
already conducted, e.g., increasing the information content by integrating future CLC
Plus products.
Author Contributions:
Conceptualization, P.D.F., F.A., and M.M.; methodology, P.D.F., A.S. and A.C.
(Alice Cavalli); software, P.D.F., A.S. and A.C. (Alice Cavalli); validation, P.D.F., A.S., A.C. (Alice
Cavalli) and A.C. (Angela Cimini); formal analysis, P.D.F. and A.C. (Angela Cimini); investigation,
P.D.F., A.S. and A.C. (Alice Cavalli); resources, M.M.; data curation, P.D.F., A.S., A.C. (Alice Cavalli)
and A.C. (Angela Cimini).; writing—original draft preparation, P.D.F. and A.C. (Angela Cimini);
writing—review and editing, P.D.F., A.S., A.C. (Alice Cavalli), A.C. (Angela Cimini), F.A. and D.S.;
visualization, P.D.F. and A.C. (Angela Cimini); supervision, M.M. and F.A.; project administration,
M.M. and F.A.; funding acquisition, M.M. and F.A. All authors have read and agreed to the published
version of the manuscript.

Land 2023,12, 286 20 of 22
Funding:
This research was funded by the Italian Institute for Environmental Protection and Research
(ISPRA) structural funds.
Data Availability Statement:
Data presented in this study are available on request from the corre-
sponding author. The data are not publicly available because they are part of ongoing research.
Conflicts of Interest: The authors declare no conflict of interest.
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