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Living Earth: Implementing national standardised
land cover classification systems for Earth
Observation in support of sustainable
development
Christopher J. Owers, Richard M. Lucas, Daniel Clewley, Carole Planque,
Suvarna Punalekar, Belle Tissott, Sean M. T. Chua, Pete Bunting, Norman
Mueller & Graciela Metternicht
To cite this article: Christopher J. Owers, Richard M. Lucas, Daniel Clewley, Carole Planque,
Suvarna Punalekar, Belle Tissott, Sean M. T. Chua, Pete Bunting, Norman Mueller & Graciela
Metternicht (2021): Living�Earth: Implementing national standardised land cover classification
systems for Earth Observation in support of sustainable development, Big Earth Data, DOI:
10.1080/20964471.2021.1948179
To link to this article: https://doi.org/10.1080/20964471.2021.1948179
© 2021 The Author(s). Published by Taylor &
Francis Group and Science Press on behalf
of the International Society for Digital Earth,
supported by the CASEarth Strategic Priority
Research Programme.
View supplementary material
Published online: 28 Jul 2021. Submit your article to this journal
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Living Earth: Implementing national standardised land cover
classication systems for Earth Observation in support of
sustainable development
Christopher J. Owers
a
, Richard M. Lucas
a
, Daniel Clewley
b
, Carole Planque
a
,
Suvarna Punalekar
a
, Belle Tissott
c
, Sean M. T. Chua
c
, Pete Bunting
a
, Norman Mueller
c
and Graciela Metternicht
d
a
Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, Wales, UK;
b
Centre for Geospatial Applications, Plymouth Marine Laboratory, Plymouth, Devon, England, UK;
c
Geoscience Australia, Symonston, ACT, Australia;
d
Earth and Sustainability Science Research Centre, School
of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia
ABSTRACT
Earth Observation (EO) has been recognised as a key data
source for supporting the United Nations Sustainable
Development Goals (SDGs). Advances in data availability and
analytical capabilities have provided a wide range of users
access to global coverage analysis-ready data (ARD). However,
ARD does not provide the information required by national
agencies tasked with coordinating the implementation of
SDGs. Reliable, standardised, scalable mapping of land cover
and its change over time and space facilitates informed deci-
sion making, providing cohesive methods for target setting
and reporting of SDGs. The aim of this study was to implement
a global framework for classifying land cover. The Food and
Agriculture Organisation’s Land Cover Classication System
(FAO LCCS) provides a global land cover taxonomy suitable
to comprehensively support SDG target setting and reporting.
We present a fully implemented FAO LCCS optimised for EO
data; Living Earth, an open-source software package that can
be readily applied using existing national EO infrastructure and
satellite data. We resolve several semantic challenges of LCCS
for consistent EO implementation, including modications to
environmental descriptors, inter-dependency within the mod-
ular-hierarchical framework, and increased exibility associated
with limited data availability. To ensure easy adoption of Living
Earth for SDG reporting, we identied key environmental
descriptors to provide resource allocation recommendations
for generating routinely retrieved input parameters. Living
Earth provides an optimal platform for global adoption of
EO4SDGs ensuring a transparent methodology that allows
monitoring to be standardised for all countries.
ARTICLE HISTORY
Received 15 April 2021
Accepted 17 June 2021
KEYWORDS
Land cover; FAO LCCS; open
data cube; sustainable
development goals;
environmental descriptors
CONTACT Christopher J. Owers chris.owers@csiro.au Commonwealth Scientific and Industrial Research
Organisation, Land and Water, Canberra, ACT, Australia
Supplemental data for this article can be accessed here.
BIG EARTH DATA
https://doi.org/10.1080/20964471.2021.1948179
© 2021 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth,
supported by the CASEarth Strategic Priority Research Programme.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
1. Introduction
The United Nations 2030 Agenda for Sustainable Development represents a global
agenda for participating nations to strive for economic, social and environmental sustain-
ability by 2030 (DESA, 2016). The Sustainable Development Goals (SDGs) were developed
to identify and monitor unsustainable practices, providing the opportunity for nations to
intervene where necessary to improve sustainable development. The SDGs include 17
thematic goals and 169 standardised targets to strive for sustainable development among
all nations, with 231 indicators to monitor performance towards agreed targets (UNGA,
2015). However, most targets designated for achievement by 2020 were not met, and
reported indicators from participating nations suggest that many will still be some way
from attainment by 2030 (Kavvada et al., 2020). A fundamental limitation in progressing
the SDGs has been identied around timely, reliable, standardised and openly available
information (UNGA, 2019). Nations have expressed concern that without key data to
support target setting and tracking of progress, through explicit information of the
performance of an indicator over time, no reasonable policy and management changes
can be actioned to change current trajectories towards attainment.
Earth Observation (EO) has been recognised as a key data source for metrics related to
the SDGs, providing global data to identify landscape types and composition and their
change over time. EO has the capacity to support reporting and tracking of approximately
40 targets and 30 indicators for many SDGs: well-developed examples include Goal 6
(Clean water and sanitation), Goal 11 (Sustainable cities), Goal 14 (Life below water), and
Goal 15 (Life on land) (EO4SDGs, 2020; Estoque, 2020; Metternicht, Mueller, & Lucas, 2020;
Paganini et al., 2018). Advances in data availability (e.g. Landsat (Woodcock et al., 2008)
and Copernicus (Berger et al., 2012) missions), storage and computational capacity (e.g.
Amazon Web Services, Google Earth Engine (see Gomes, Queiroz, & Ferreira, 2020)), and
analytical capabilities (e.g. Open Data Cube (Killough, 2018), Machine learning (see
Ferreira, Iten, & Silva, 2020)) have provided a wide range of users access to global cover-
age analysis-ready data (ARD). However, ARD does not provide the information required
by national agencies tasked with coordinating implementation of SDGs (Kavvada et al.,
2020). Instead, they require standardised and informative end user products derived from
ARD to track progress towards agreed targets. This includes land cover and its change
over time – detailed information that contributes to the mapping and reporting on 14 of
the 17 SDGs (EO4SDGS, 2020). However, many nations lack access to an operational,
standardised land cover product.
Land cover maps are an essential information component for planning and managing
sustainable development, often utilised to establish baseline conditions against which to
monitor change across a range of spatial, temporal and thematic scales (Gómez, White, &
Wulder, 2016; Rogan & Chen, 2004). Operational monitoring of land cover requires timely,
reliable and repeatable mapping over multiple time-steps and at spatial scales relevant to
policy and management (Franklin & Wulder, 2002). Robust methods that allow seamless
integration of new observations or data and a high degree of condence for change
detection are greatly valued. However, most existing products do not provide the opera-
tional requirements for SDG target setting and reporting at a national level, and many are
also not comparable between countries (Metternicht et al., 2020). In addition, existing
global and continental land cover maps are often produced at spatial scales not suitable
2C. J. OWERS ET AL.
for SDG reporting units. These include IGBP DISCover (1 km; Loveland et al., 2000), UMD
Land Cover (1 km; Hansen, DeFries, Townshend, & Sohlberg, 2000), GlobCover (300 m;
Arino et al., 2008), Corine Land Cover (300 m; Bossard, Feranec, & Otahel, 2000), ESA CCI
Land Cover (300 m; Bontemps et al., 2013), and MODIS Land Cover (250 m; Friedl et al.,
2010). Challenges associated with high resolution land cover mapping at large scales are
diminishing with increased data availability and computational capacity (e.g. Global 30 m,
Chen et al., 2014; Europe 10 m, Venter & Sydenham, 2021) and attention is now shifting to
harmonise land cover maps (Yang, Li, Chen, Zhang, & Xu, 2017). A component of this is to
adopt systems for mapping land cover that are consistent terminologically (e.g. forest vs
woodland), semantically (e.g. trees are plants > 2 m height) and cartographically (e.g. map
products are comparable). This is becoming of increased importance given enhanced
capacity for mapping land cover across large areas and on a repeat basis (e.g.
Calderón-Loor, Hadjikakou, & Bryan, 2021; Li, Qiu, Ma, Schmitt, & Zhu, 2020).
To comprehensively support international initiatives for sustainable development, land
cover maps must prioritise methods that are transparent (i.e. FAIR principles; Wilkinson
et al., 2016) and transferable (e.g. across sensors and platforms, utilising available com-
putational resources), with consistent semantics and taxonomies to facilitate robust and
routine generation. The Land Cover Classication System (LCCS), developed by the Food
and Agriculture Organisation (FAO; Di Gregorio & Jansen, 2000), provides a taxonomy that
is fundamentally well suited to consistent classication of land cover. The FAO LCCS
attempts to x historical issues of semantics with land cover classications, identifying
the need to align landscape descriptions with their “mapability” (Di Gregorio, 2016). LCCS
is a semantically-driven integrated system, providing a taxonomy with a high level of
descriptive detail that is consistent and comparable at dierent scales and over time, and
applicable to any geographic location globally. As an internationally recognised taxon-
omy, land cover maps using the LCCS taxonomy are also interoperable with end-user
requirements (i.e. classes generated closely align with habitat taxonomies that are widely
used by ecologists) (Atyeo & Thackway, 2006; Kosmidou et al., 2014).
Application of the FAO LCCS for use with EO data has been established using the Earth
Observation Data for Ecosystem Monitoring (EODESM) system (Lucas & Mitchell, 2017;
Lucas et al., 2019, 2020). Unlike other EO implementations of the LCCS, which generally
base their classications on the “end classes” in the LCCS taxonomy, the EODESM system
follows the sequence of classications through the hierarchy using products derived from
EO data. Rather than focusing on providing the best classication algorithm, the EODESM
system places emphasis on retrieving continuous and categorical environmental descrip-
tors; biophysical input variables with predened units or categories (see Lucas & Mitchell,
2017; Lucas et al., 2019; Planque et al., 2020). These are then combined subsequently to
construct the LCCS classes. The advantage of this classication approach is that it is
relevant and applicable to any site globally and can be applied independent of scale
and time. EODESM demonstrated the global applicability of the LCCS taxonomic frame-
work with an initial focus on national parks (Lucas & Mitchell, 2017), as well as sites in
Australia (Lucas et al., 2019) and Malaysia (Lucas et al., 2020) and most recently for Wales
(Planque et al., 2020).
The FAO LCCS system has been fully designed and comprehensively documented
(LCCS-2: LCCS software version 2; Di Gregorio, 2005). However, no systematic imple-
mentation is available for EO data. EODESM demonstrates the capacity to implement
BIG EARTH DATA 3
a fully interoperable EO software product for application. Several prior land cover
products recognise the exibility and comprehensive nature of LCCS and have imple-
mented some aspects of the LCCS-2 on a t-for-purpose basis (e.g. GlobCover, Bicheron
et al., 2008; Dynamic Land Cover Dataset, Lymburner, Tan, Mueller, Thackway, &
Thankappan, 2011; North American Land Change Monitoring System, Latifovic et al.,
2012). Notably, several semantic issues are not fully resolved with LCCS-2 that have
remained a challenge for EO implementation, often requiring users to modify taxonomic
classes to suit requirements or only adopt LCCS-2 taxonomies and not the hierarchical-
modular structure. Resolving semantic challenges with LCCS-2 for EO application would
encourage widespread adoption and reduce barriers to using the LCCS-2 system in its
entirety.
The aim of this study was to implement a global framework for classifying land cover in
support of consistent and comparable reporting on the SDGs. The FAO LCCS provides
a global land cover taxonomy suitable to comprehensively support SDG target setting
and reporting. We present a fully implemented FAO LCCS-2 optimised for EO data; Living
Earth, an open-source software package that can be readily applied using existing
national EO infrastructure and satellite data. To ensure easy adoption of Living Earth for
SDG reporting, we identied key environmental descriptors of FAO LCCS-2 to provide
recommendations on resource allocation for generating routinely retrieved input para-
meters. In addition, we examined two national implementations using dierent EO
infrastructure and satellite data, Australia and Wales (UK), providing recommendations
on resource allocation for further development.
2. Methods
2.1. FAO LCCS-2 and EODESM
The FAO LCCS-2 framework is hierarchical, consisting of a dichotomous and
a modular-hierarchical phase. The dichotomous phase is a binary decision tree
providing eight (8) output classes that determine broad landscape types (Figure 1).
At level 1 (L1), areas that are primarily vegetated are dierentiated from those that
are primarily non-vegetated. Terrestrial and aquatic areas are subsequently dieren-
tiated at level 2 (L2). Primarily vegetated areas are further classied based on human
activities, generating four primarily vegetated level 3 (L3) classes including a) culti-
vated and managed terrestrial areas, b) natural and semi-natural vegetation, c)
cultivated aquatic or regularly ooded areas, and d) natural and semi-natural aquatic
or regularly ooded vegetation. Similarly, primarily non-vegetated areas are sepa-
rated into a) articial surfaces and associated expanses, b) naturally bare areas, c)
natural water bodies, and d) articial water bodies.
The subsequent modular-hierarchical phase (referred hereon in as level 4; L4) provides
increasingly detailed landscape descriptions tailored to each of the broad land cover
types across the eight level 3 classes (Figure 1). In this phase, the generation of the land
cover class is given by combining a set of predened land cover classiers that also
operate in a hierarchy as level 4 “tiers”. The classication system generates mutually
exclusive land cover classes, which comprise a unique boolean formula (a coded string
of classiers used) and a structured description of the land cover class based on level 4
4C. J. OWERS ET AL.
Cultivated/
Managed
Vegetative cover of
human origin
requiring long term
maintenance
(Semi) Natural
Vegetative cover in
balance with abiotic
and biotic forces of its
biotope
Cultivated/
Managed
Aquatic crop standing
in water over
extensive periods
during cultivation
(Semi) Natural
Vegetative cover
where the water table
is usually at or near
the surface
Artificial
surfaces
Artificial cover as a
result of human
activities
Bare areas
No artificial cover as
a result of human
activities
Natural
waterbodies
Areas naturally
covered by water
Artificial
waterbodies
Artificial as a result
of human activities
Lifeform
Terrestrial
Vegetation influenced by the edaphic
substratum
Terrestrial
The cover influenced by the edaphic
substratum
Aquatic
Vegetation influenced by the presence
of water over extensive periods of time
Aquatic
Cover influenced by the presence of
water over extensive periods of time
Primarily vegetated
Vegetative cover of at least 4% for at least two months of the year
Primarily non-vegetated
Vegetative cover of less than 4% f or more than 10 months of the year
Spatial distribution
Cover Height
Spatial size
Crop sequence Water supply Time factor
Crop combinations Crop lifeform #2* Crop lifeform #3*
Leaf type Leaf phenology Urban vegetation*
Lifeform
Spatial distribution
Cover Height
Second layer Lifeform #2 Cover #2
Leaf type Leaf phenology Leaf phenology*
Height #2
Lifeform Cover Height
Daily water supply
Spatial distribution Spatial size
Crop sequence
Lifeform Lifeform* Cover Height
Water seasonality
Second layer Lifeform #2 Cover #2 Height #2
Leaf type Leaf phenology Leaf phenology*
Bare surface Surface materials* Unconsolidated* Hardpans*
Macropattern
Linear Non-linear Artificial density Non built-up
Artificial surface
Water state
Water depth Water movement Sediment load Substrate type
Water persistence Intertidal areas Snow persistence
Water state
Water depth Water movement Sediment load Substrate type
Water persistence Snow persistence Interti dal areas
L1 L4L2 L3
T1
T2
T3
T4
T5
T1
T2
T3
T4
T1
T2
T3
T4
T1
T2
T3
T4
T1
T2
T1
T2
T1
T2
T3
T1
T2
T3
Figure 1. The Living Earth LCCS-2 implementation. The hierarchy for the dichotomous phase (L1 – L3)
visualised vertically and the modular-hierarchical phase (L4) visualised horizontally. Each level 3 broad
land cover type has associated level 4 additional descriptors that are also hierarchical (i.e. between 2–5
tiers). T; tier L; level. Asterisk (*) indicates land cover classes not required for subsequent environ-
mental descriptors in the hierarchy.
BIG EARTH DATA 5
tiers. At any position in the hierarchy the user can stop, and a mutually exclusive class is
generated. The system created is a highly exible a priori land cover classication in which
each category is clearly and systematically dened to provide internal consistency.
2.2. Living Earth: LCCS-2 optimised for EO
The design of Living Earth closely followed the LCCS-2 documentation (Di Gregorio, 2005)
to maintain the fundamental principles and qualities of LCCS semantics and its taxonomic
framework. This included maintaining the LCCS structure, dichotomous and modular-
hierarchical phases, and broad land cover types with additional descriptors. Several
modications were made from LCCS-2 environmental descriptors for the implementation
of Living Earth, with these focused on optimising LCCS-2 for readily available EO data. To
ensure easy adoption for end users, we considered a practical data driven approach to
implementing LCCS-2, in particular the exibility and “mapability” of the system (Di
Gregorio, 2016). The intent of any additional environmental descriptors was examined
carefully to ensure they enhance the overall description of the land cover class. All
modications and assumptions undertaken are described below.
2.2.1. Key modifications and assumptions
The FAO LCCS level 4 as a hierarchical design is composed of tiers, whereby preceding
land cover descriptors must have input data before additional environmental descriptors
can be added (Figure 1). These tiers are also interdependent, where a landscape class (i.e.
lifeform) is required before additional information within the same tier can be added to
the landscape description (i.e. cover). Living Earth maintains the hierarchy of level 4
descriptions, however does not require interdependency within tiers. Specically, the
generation of routinely derived descriptors for some classes are already achievable from
EO data and provide valuable landscape information (e.g. vegetation cover and height).
Importantly, further landscape descriptions for a proceeding tier still require all classes of
the preceding tier to be valid. For example, classes at tier 1 of level 4 for terrestrial
vegetated areas, that is lifeform, cover and height, are not dependent on each other for
a valid landscape description. However, all are required to progress to tier 2 descriptors.
Inherent dependencies within these classes are still relevant, for example, the class “trees”
in the category “lifeform” cannot be assigned to the class “height < 2 m”.
The FAO LCCS denition of vegetation strata is an ecological denition manifest
through relationships of vegetation lifeform, cover and height. This can be dicult to
determine from EO and particularly dependent on the approaches to generate lifeform,
cover and height metrics. Dening strata consistently is critical, as this impacts the
assignment of land covers to several strata classes (e.g. lifeform, cover and height
of second strata as well as crop combinations and crop lifeforms). To optimise for the
use of EO to generate consistent and comparable landscape descriptors for a variety of
landscapes, we only use height to dierentiate the second strata. For example, if the rst
strata are lifeform of trees 2–5 m in height, the second strata must be less than 2–5 m in
height and is therefore not a sub strata of tree vegetation.
Living Earth landscape descriptions do not assume all data are available and therefore
can provide landscape classications with partial LCCS-2 level 4 descriptions. The FAO
LCCS has approximately 12,000 unique complete landscape descriptions, assuming all
6C. J. OWERS ET AL.
required input data are available. Due to the inherent limitations of EO data, as well as
ongoing research to retrieve or classify environmental descriptors, it is impractical to
expect all data requirements for a complete level 4 landscape description. Living Earth
therefore provides an accessible data-driven approach to describe environmental land-
scapes, where valid and useful landscape descriptors can be produced with available data.
This allows greater exibility to the LCCS framework and encourages greater uptake for
land cover classication.
2.2.2. Technical modifications
To align the software design and implementation with the LCCS-2 in the most eective
yet simplistic way possible, while ensuring LCCS remains intuitive, several technical
modications were employed. These are detailed briey here and extensively documen-
ted in the software code.
Alphanumeric codes align to terrestrial (semi) natural vegetation
LCCS descriptors are a concatenation of alphanumeric codes that detail each level 4
category contributing to the description (e.g. A12.A1.A10.B5.C1). Alphanumeric codes in
LCCS-2 of a level 4 category may not be identical for each level 3 broad land cover type
(e.g. tree lifeform is A1 for Cultivated and managed, yet is A3 for (semi) natural). These
vary for each level 3 class, where a level 4 class attributes descriptors to multiple level 3
classes (i.e. lifeform, cover, height). All level 4 codes in Living Earth are aligned to
terrestrial (semi) natural vegetation. This provides consistency within level 4 classes
and eciency for input layers and concatenation in level 4 classication. In addition,
several level 4 categories were merged to simplify the classication (i.e. one lifeform
layer input is used for classifying lifeform of all vegetated level 3 classes) as well as
broad categories removed in favour of specicity (e.g. cover classes closed to open 15–
100% and 40–100% are not useful ecological categories to determine from EO). All are
documented in the software code for each level 4 class to show deviation from FAO
LCCS-2.
Class categorical boundaries altered to non-overlapping ranges
FAO LCCS-2 utilises overlapping class boundaries for several continuous inputs (e.g. cover:
closed > 60–70%). This represents the ambiguity associated with quantitative measure-
ment and meaningful ecological disaggregation of environmental descriptors. Living
Earth is optimised for EO, requiring distinct class boundaries for meaningful implementa-
tion of mapping. Class categorical boundaries were altered to give non-overlapping
ranges, centred on the middle of the FAO LCCS-2 range (i.e. LCCS-2, > 60–70%: Living
Earth, > 65%). This modication was introduced for all relevant classes including cover,
height, and second strata cover and height.
Additional environmental descriptors and attribution
LCCS-2 level 4 classes were reviewed to optimise for EO inputs. A new class for tidal areas
was generated, separating these from the water persistence categories because a) tidal
areas can be perennial/non-perennial and thus may conict with water persistence
categories and b) EO-derived products available to identify tidal areas are increasingly
BIG EARTH DATA 7
being generated on a routine basis (e.g. Bishop-Taylor, Sagar, Lymburner, & Beaman,
2019; Sagar, Roberts, Bala, & Lymburner, 2017).
Living Earth includes height and cover attributes for cultivated and managed areas.
These are not included in LCCS-2; however, they were deemed useful environmental
descriptors that could be retrieved from EO. Moreover, several agricultural descriptors can
be dicult to derive from EO data and including height and cover helps to provide some
description of the cultivated landscape with a reasonable degree of accuracy.
2.3. Software design
Living Earth was designed as an open-source Python library, built on top of xarray (Hoyer
& Hamman, 2017) and NumPy (Harris et al., 2020), and utilising other established Python
libraries for data import and export, such as GDAL (GDAL/OCR Contributors, 2021),
Rasterio (Gillies, 2019) and Open Data Cube (Killough, 2018; Killough, Siqueira, & Dyke,
2020). We followed high standards of software design, including version control and unit
testing for LCCS classication outputs. The software design was based on applicability for
easy adoption and understanding for a broad range of end users, as well as LCCS structure
and modications based on EO implementation (Figure 2).
Living Earth provides high data input exibility, with modules interfacing with GDAL (via
rasterio), Open Data Cube (ODC) and RIOS (for object-based classication using raster
attribute tables; Gillingham & Flood, 2014). Initially, 5 binary input datasets are required
for the level 3 classication. These include a vegetated/non-vegetated layer, water/non-
water layer, cultivated/natural vegetation layer, articial surface/bare areas layer, and
Figure 2. Software design schematic showing ingestion of the data and application of rules to produce
level 3 and level 4 classification outputs as both data (8-bit/n-band raster) and coloured RGB images
for visualisation.
8C. J. OWERS ET AL.
articial water/natural water layer. The level 3 classication is then simply a concatenation of
the 5 input layers in the hierarch to derive 8 broad landscape types. An 8-bit raster, coded
with LCCS-2 level 3 values (i.e. 111, 112, 123, 124, 215, 216, 227, 228) and three band image
(RGB), coloured by class for visualisation, are provided as an initial output.
Level 4 input layers can be categorical or continuous (i.e. cover, height, urban density),
where continuous are converted to categorical denitions as specied by LCCS-2 (unless
altered as stated in section 2.2). Each level 4 layer is then applied to the relevant level 3
category where any dependencies on other level 4 layers are met. Valid level 4 landscape
descriptions are conrmed via unit testing. Level 4 classication is then a concatenation of
the level 4 layers, with this providing unique alphanumeric landscape descriptions. An
n-band raster, representing each level 4 class input as a single band and RGB image, with
each class coloured based on the Living Earth LCCS Level 4 colour scheme, are provided as
a nal output.
2.4. Key environmental descriptors
Key environmental descriptors were identied for Living Earth using variable importance
scores. Variable importance was dened as the reoccurrence of an input layer to produce
all outputs for each broad landscape type, calculated by summing the total times
categories from an input class were used divided by the total number of unique outputs.
A relative variable importance score was calculated for each input variable for each broad
landscape. As a consequence of the large number of input combinations, a python
workow was developed that ran the Living Earth system by randomly selecting from
all possible input variables for each broad landscape types provided from level 3. These
were then used to generate unique output class identier codes with the associated
description. For each level 3 class, 10,000 random selections (samples) were undertaken
per run and the classication was run 1000 times, with this generating up to 10 million
LCCS-2 land cover class combinations. When no new output classes were found, the
workow terminated.
3. Results
3.1. Software design
Living Earth provides a fully implemented FAO LCCS-2 optimised for EO. Current data
ingest classes allow the classication to be applied to any rasterised spatial data (e.g.
Landsat, Sentinel-1/2, Lidar derived surfaces, airborne imagery, drone imagery), with the
capacity to apply the classication scheme to non-raster data (e.g. tables, databases). The
plugin architecture of landscape descriptors at level 4 allows for the addition of environ-
mental descriptors pertinent to each use case. Moreover, landscape classications can
occur with limited data input, and all inputs do not need to be present to generate a valid
unique landscape description. Living Earth has been optimised for high-performance
computing, with tested compatibility on several national super-computing facilitates
(e.g. Australia’s National Computational Infrastructure (NCI), Supercomputing Wales)
and cloud services (e.g. Amazon Web Services (AWS)). This is particularly useful for
national implementations of LCCS that require a routine and exible workow. Living
BIG EARTH DATA 9
Earth is an open-source software package under Apache 2.0 license, available on bit-
bucket (https://bitbucket.org/au-eoed/livingearth_lccs).
3.2. Living Earth: LCCS-2 optimised for EO
FAO LCCS provides approximately 12,000 unique landscape descriptions through combina-
tions of level 4 inputs. Living Earth provides approximately 573,307 unique landscape
descriptions utilising the same fundamental framework. The pronounced increase in unique
descriptions is attributed to the key modications to optimise for EO implementation (section
2.2). Unique landscape descriptions specic to vegetation accounted for > 99% of all unique
descriptors, with non-vegetative classes providing only 720 unique landscape descriptions
(Table 1). All unique landscape descriptors are provided in the supplementary material.
3.3. Key environmental descriptors
Key environmental descriptors reect the Living Earth classication hierarchy. Broadly,
variables of greater importance were positioned at tier 1 and tier 2, utilised in many
landscape descriptions for each level 3 landscape type (Figure 3). Tier 1 for vegetated land
cover (lifeform, cover, height) were equally important environmental descriptors for any
vegetated land cover (between 9–16%). Daily water supply and water seasonality for
aquatic vegetation were identied as particularly important descriptors, closely important
to lifeform, cover and height attributes. This is expected as edaphic conditions are the
primary dierentiation of terrestrial and aquatic vegetation. Variable importance was also
dependent on how many categories occur within each level 4 class, where more cate-
gories result in greater number of unique outputs and hence greater variable importance
score for relevant class. Modiers and other classes not required for proceeding tiers (e.g.
urban vegetation, phenology, lifeform modications) were among the least important
descriptors for unique land cover classes. The second strata information was of lower
importance due to considerable preceding information to be derived.
For non-vegetated classes, tier 1 landscape attributes dominated the unique landscape
descriptions for articial surfaces and bare areas, accounting for > 40% of unique descrip-
tions (Figure 3). Water state explains > 20% of descriptors, with water persistence and
depth contributing 17% and 21% respectively. Surprisingly, water depth is of greater
importance than any tier 2 variables, despite being a tier 3 variable.
For implementing Living Earth and deriving environmental descriptor inputs, variable
importance analysis identies priority inputs for landscape descriptions. For vegetation,
deriving lifeform, cover and height are of highest priority. For aquatic vegetation, attri-
butes of edaphic conditions (daily water supply, water seasonality) should be derived
subsequently. Spatial information should be the proceeding focus, such as spatial dis-
tribution, spatial size and the presence of second strata. For non-vegetated terrestrial
areas, priority should be dierentiating articial surface types (i.e. built up, non-built up,
linear, non-linear) and bare surface types (i.e. consolidated, non-consolidated, bare rock,
hardpans, loose and shifting sands) as this will directly inform proceeding tier attributes.
For waterbodies, focus on water state (i.e. water, snow, ice), and subsequently, environ-
mental descriptors of water persistence and water depth should be prioritised.
10 C. J. OWERS ET AL.
Table 1. Living Earth unique landscape descriptor codes and example descriptions for each level 3 landscape type.
Level 3
landscape
type
Unique
landscape
descriptors Alphanumeric example Landscape description example
Terrestrial Cultivated/Managed 466699 A11.A1.A10.B10.D1.E1.B8.B1.C1.D6.D8
A11.A5.A16.B11.D4.E6.B6.B4.C2.D1
Cultivated Terrestrial Vegetated: Woody Closed (> 65%) (< 0.5 m) Broad-leaved Evergreen
Medium to large (> 2 ha) field(s) Single crop Irrigated Drip Fallow system
Cultivated Terrestrial Vegetated: Forbs Scattered (1 to 4%) (0.8 to 3 m) Scattered (clustered)
Medium (2–5 ha) field(s) Multiple crop Cultural Practice (Rainfed)
Terrestrial (Semi) Natural 21979 A12.A1.A10.B5.D1.E2.C2.F2.F10
A12.A4.A13.B9.D2.E3.C1.F2.F6.F10
Natural Terrestrial Vegetated: Woody Closed (> 65%) (> 14 m) Broad-leaved Deciduous
Fragmented Second layer present Sparse (1 to 15%)
Natural Terrestrial Vegetated: Shrubs Open (15 to 40%) (0.5 to 2 m) Needle-leaved Mixed
Continuous Second layer present Herbaceous Sparse (1 to 15%)
Aquatic Cultivated/Managed 7970 A23.A1.A10.B10.C1.B8.B1.D2
A23.A2.A16.B13.C3.B6.B1.D3
Cultivated Aquatic Vegetated: Woody Closed (> 65%) (< 0.5 m) Water (persistent for whole day)
Medium to large (> 2 ha) field(s) Relay intercropping
Cultivated Aquatic Vegetated: Herbaceous Scattered (1 to 4%) (0.03 to 0.3 m) Waterlogged
Scattered (clustered) Medium to large (> 2 ha) field(s) Sequential cropping
Aquatic (Semi) Natural 75939 A24.A1.A15.B9.C4.D2.E1.F2.F3.F8.G10
A24.A5_A9.A15.B11.C1.D4.E6_E6
Natural Aquatic Vegetated: Woody Sparse (4 to 15%) (0.5 to 2 m) Water > 3 months (persistent
all day) Needle-leaved Evergreen Second layer present Woody Closed (> 65%) (< 0.5 m)
Natural Aquatic Vegetated: Forbs Free floating forbs Sparse (4 to 15%) (0.8 to 3 m) Water >
3 months (semi-) permanent (Annual)
Artificial surfaces 23 B15.A2_A5
B15.A4_A13_A14
Artificial Surface: Non-built up Waste dump deposit
Artificial Surface: Non-linear infrastructure Urban areas High (> 75%) density
Bare areas 89 B16.A2.B13
B16.A6.B9
Natural Surface: Unconsolidated Salt flat
Natural Surface: Loose and shifting sands Parabolic dunes (unsaturated)
Artificial waterbodies 304 B27.A1.B1.C1_A4
B27.A2.B9.C2_B4
Artificial Water: (Water) Perennial (> 9 months) Medium to deep (> 2 m) Flowing water
Artificial Water: (Snow) Non-perennial (1 to 3 months) Shallow (< 2 m) Bare rock substrate
Natural waterbodies 304 B28.A1.B1.C1_A4
B28.A2.B9.C2_B4
Natural Water: (Water) Perennial (> 9 months) Medium to deep (> 2 m) Flowing water
Natural Water: (Snow) Non-perennial (1 to 3 months) Shallow (< 2 m) Bare rock substrate
BIG EARTH DATA 11
Figure 3. Variable importance of level 4 environmental descriptors for each level 3 broad landscape
type. Variable importance is calculated as the number of times categories from the input class were
used divided by the total number of unique outputs. Asterisk (*) indicates land cover classes not
required for subsequent environmental descriptors in the hierarchy.
12 C. J. OWERS ET AL.
4. Discussion
This study showcased the fully implemented FAO LCCS-2, Living Earth, optimised for EO
application. Living Earth was developed to align with FAIR principles of software and data
dissemination as an open-source system intended to utilise free and available EO data.
The classication of land cover can be applied to any rasterised spatial data, independent
of spatial and temporal resolution, as well as direct functionality with the Open Data Cube.
The plugin design of Living Earth allows easy addition of environmental descriptors
pertinent to the use case. Living Earth provides a framework for standardised, globally
applicable and comparable land cover classication to support EO4SDGs. To aid nations in
adopting Living Earth for SDG target setting and reporting, key environmental descriptors
were identied to direct resource allocation so that the most important input data are
generated in order of ease and priority. Living Earth has been implemented in Australia
and Wales (UK) and will be examined here to provide a roadmap for both nations as well
as indicative examples for others reporting on SDGs.
4.1. Living Earth: LCCS-2 optimised for EO
The FAO LCCS-2 provides a consistent and easily interpretable semantic framework for
global application, describing approximately 12,000 variations in landscape types.
However, there was a substantial need to modify the LCCS-2 to optimise its use for EO
inputs and subsequent production of spatially explicit maps. Key modications needed
for Living Earth signicantly increased the number of unique landscape descriptions,
which approximated 573,307 (almost 50 times more). These included vegetation strati-
cation based on height, the inclusion of height and cover in cultivated/managed taxo-
nomies, and moderate relaxation of hierarchical dependencies with unique classication
descriptions in order to provide valid LCCS-2 outputs with limited data inputs. The
pronounced increase in unique landscape descriptions occurred because of hierarchical
attribution of landscape descriptions at level 4, whereby modifying key classes in the
hierarchy increased unique class outputs several fold. For example, the addition of cover
and height descriptions to the cultivated/managed classes at tier 1 eectively increased
the number of unique output classes of cultivated/managed LCCS-2 classes by 45 times (5
cover categories, 9 height categories).
Modications from LCCS-2 were considered with two criteria; is the modication a)
necessary for EO implementation or b) utilising EO data to enhance landscape descrip-
tions? For example, vegetated categories in LCCS-2 require lifeform as a prerequisite for
attribution of vegetation cover and height. However, the generation of vegetation cover
and height from EO is more accessible than lifeform at a range of spatial scales. Vegetation
cover and height can be measured directly using EO (Lang et al., 2021; Liao, Van Dijk, He,
Larraondo, & Scarth, 2020; Los et al., 2012; Potapov et al., 2021), however lifeform
derivatives often requires some inference or proxy (often using cover and height, e.g.
Scarth, Armston, Lucas, & Bunting, 2019; Schneider et al., 2020). Removing dependencies
on lifeform for vegetation cover and height enhanced landscape descriptions provided by
LCCS-2 as these environmental descriptors provide sucient information that is highly
desirable. Further modications to LCCS-2 dependencies were carefully considered to
ensure LCCS-2 semantics and taxonomic framework were not undermined. Dependencies
BIG EARTH DATA 13
that were clearly required to give meaningful context to additional descriptors, that
would otherwise be unhelpful when interpreted by an end user, were not altered. For
example, spatial distribution requires lifeform, cover and height to give context to why
spatial heterogeneity may be important, such as fragmentation of a woodland over time.
4.2. Key environmental descriptors
Identifying key environmental descriptors for Living Earth is helpful for resource allocation
and provides a clear pathway for implementation. Output land cover classes in Living
Earth are predened by combining inputs layers, therefore allowing users to focus on
generating the most useful layer required as an input. The interchange of input layers, as
a function of increased accuracy or precision, enables very eective ongoing maintenance
and implementation of the land cover system, as landscape descriptors are not altered
from the previous implementation, rather just improved. This facilitates reliable land
cover comparisons through space and time, accommodating (and benetting from) the
latest technological and/or computational advances. Key environmental descriptors iden-
tied in this study provide specic guidance for users and nations as a pathway for
implementation. These priorities will likely be the priorities for diverse and complex
landscapes globally, however national implementations may require shifted priorities as
appropriate to the landscape.
The natural landscape, particularly vegetated classes, present the most diverse land-
scape descriptions, accounting for > 99% of all unique descriptors in Living Earth.
Generation of tier 1 inputs for vegetated systems should be prioritised (i.e. lifeform,
cover and height). Lifeform is a category that can be challenging to generate from EO
data, particularly beyond the classes of woody and herbaceous (i.e. trees, shrubs, forbs,
graminoids, lichens and/or mosses). For this, a number of methods have been used to
generate the categories, including well-developed machine learning approaches (e.g.
Vegetation Fractional Cover, Gill et al., 2017; Hill & Guerschman, 2020; Woody Cover
Fraction, Liao et al., 2020), or inherent qualities of sensors such as C-band backscatter
characteristics (Planque et al., 2021). However, based on the FAO LCCS denitions of
lifeform, the best approach is to use continuous raster height products derived from, for
example, airborne or spaceborne interferometric SAR or Lidar (e.g. ICESAT or GEDI;
Potapov et al., 2021; Schneider et al., 2020; Simard, Pinto, Fisher, & Baccini, 2011) as the
provision of a unit measure (i.e. height in metres) provides a dened threshold for
dierentiating some lifeforms (e.g. trees > 2 m, shrubs < 2 m). Cross tabulations of height
and cover also provide the basis for dening forests (e.g. FAO, 2020; Sasaki & Putz, 2009)
and generating structural classications (e.g. Scarth et al., 2019) that can be described
according to lifeform, if categorised correctly. We implore users that the generation of
lifeform, cover and height of vegetation is the most important metrics for input into Living
Earth and this can be achieved using established methods and available EO data.
For non-vegetated classes, resources should focus on categories of water state (i.e.
water, snow, ice) and subsequently environmental descriptors of water persistence and
water depth. Detection of water bodies is readily achieved using data from optical sensors
(e.g. Mueller et al., 2016) and SAR (e.g. Sentinel-1; Huang et al., 2018). In addition, the
routine retrieval of identifying waterbodies facilitates time-series approaches to identify-
ing water persistence over time (Krause, Newey, Alger, & Lymburner, 2021; Mueller et al.,
14 C. J. OWERS ET AL.
2016; Sagar et al., 2017). Water metrics are vital for landscape management globally, and
this input to Living Earth represents an important component that should be a priority for
implementation.
For terrestrial surfaces, focus should be on dierentiating articial surfaces types (i.e.
built up, non-built up, linear, non-linear) and bare surface types (i.e. consolidated, non-
consolidated, bare rock, hardpans, loose and shifting sands). However, these can be
challenging to classify from EO data, particularly on a routine basis. Dierentiation of
articial surface types has been achieved using object-based classications, with some
established methods and demonstrations showing success, albeit varying substantially
with sensor type (Chen et al., 2014; Ma et al., 2017; Myint, Gober, Brazel, Grossman-Clarke,
& Weng, 2011). For naturally bare areas, several existing products, such as geological and
sedimentary mapping, could be utilised. However, routine retrieval of these products is
challenging, particularly as spectral properties exploited for geological and sedimentary
mapping may not correspond to bare surface types (Post et al., 1994; Roberts, Wilford, &
Ghattas, 2019).
4.3. Living Earth for Australia
Australia’s current infrastructure and strategic direction to utilise EO data are highly
compatible with Living Earth for mapping the Australian landscape. Digital Earth
Australia (DEA) is an ODC instance containing the Australian archive of Landsat data
(1987 to present) (Lewis et al., 2017). The ODC framework enables a pixel-based approach,
rather than a traditional scene-based approach to analysing Landsat data, providing direct
comparison of observations from specic locations acquired at two or more epochs (Dhu
et al., 2017). This analytical power provides unprecedented capability for continental-scale
analysis at a high temporal frequency and has been used to develop several innovative
products (see Bishop-Taylor et al., 2019; Mueller et al., 2016; Roberts, Dunn, & Mueller,
2018; Roberts, Mueller, & McIntyre, 2017; Roberts et al., 2019; Sagar et al., 2017).
Key environmental descriptors required for future development and application of
Living Earth for Australia can be identied with knowledge of the unique landscape types
and their likely changes, such as impacts of wildre, identied through vegetation life-
form, cover and height change, as well as ood and drought, through water seasonality
and persistence over time. Several environmental descriptors needed to construct the
level 4 classes have already been generated at a national level and include broad
continuous lifeform (woody, herbaceous) (Liao et al., 2020), vegetation cover via fractional
cover metrics (Gill et al., 2017; Hill & Guerschman, 2020), and water persistence for
identifying temporal water dynamics in the landscape (Mueller et al., 2016). Australia’s
landscape is dominated by natural vegetated areas and retrieval of input for Living Earth
should prioritise development of vegetation height and cover metrics through data from
spaceborne Lidar (e.g. ICESAT and GEDI). In addition, temporal water dynamics are
important for Australian landscape change, and biophysical parameters such as water
state, seasonality, and persistence should also be prioritised. Of lesser priority are other
environmental descriptors such as leaf type and leaf phenology, as Australia’s native
vegetation is dominated by evergreen species.
Australia aims to continue to report on many SDG targets, recently identied through
a national review (DFAT, 2018). Several SDG indicators have been identied where the
BIG EARTH DATA 15
LCCS can provide essential metrics for input (Metternicht et al., 2020), including SDG
targets 6.6.1 (change in the extent of water-related ecosystems over time), 11.3.1 (ratio of
land consumption rate to population growth rate), and 15.3.1 (proportion of land that is
degraded over total land area). Ongoing work has been presented on 15.3.1 (Sims, Barger,
Metternicht, & England, 2020; Sims et al., 2019) demonstrating a best practice approach,
where reporting on land degradation should also include processes responsible for
degradation. Living Earth oers this capacity with its additive attribute of level 4. This
allows, for instance, forest degradation to be identied through changes in vegetation
lifeform, cover or height rather than high-level change from vegetated to non-vegetated
landscapes. This type of approach with multiple lines of evidence for degradation aligns
with the interpretation matrix presented in Sims et al. (2020) and good practise guidance
(Sims et al., 2019). Current developments of Living Earth for Australia, together with
identied key environmental descriptors, have the potential to achieve best practise
reporting for the SDG 15.3.1 at a national scale, with spatial and temporal resolutions
suitable for measuring and reporting. The adoption of Living Earth for Australia’s SDG
reporting would provide a standardised, comparable system for condent estimates of
change, aligning currently reported on targets and providing a means to report on
additional targets where data sources have not been identied yet.
4.4. Living Earth for Wales (UK)
Wales’ current and emerging EO infrastructure and data sources have provided an ideal
opportunity to adopt Living Earth. Land cover mapping using Living Earth compliments
the use of freely available EO data to provide an entire open-source framework, coupled
with the facilities of high-performance computing (Supercomputing Wales) to analyse,
process and classify dense time-series of satellite sensor data. In particular, Sentinel-1
provides a very useful temporal dataset for Wales and the broader UK due to data
collection independent of cloud cover. Retrieval of environmental descriptors relevant
to Living Earth have recently been demonstrated, including semi-natural vegetation
extent (Punalekar et al., 2020), identication of water bodies and water seasonality
(Planque et al., 2020), and species-level crop type classication (Planque et al., 2021).
Wales represents a highly modied and complex landscape dominated by pastureland,
woodland and urbanised settings (Lucas et al., 2006). Seasonality and episodic events,
such as ooding and severe storms, as well as forestry and clear-cutting activities, are
common pressures of landscape change in Wales (Planque et al., 2021). Identifying life-
form is of primary importance and facilitates dierentiation of environmental descriptors
important for major natural resource (e.g. forestry and national park assets) and agricul-
tural land management. Vegetation cover and height are also key environmental descrip-
tors for both major management activities due to seasonality-inuenced cover and height
changes due to felling and regrowth forestry operations (Punalekar et al., 2020). As the
landscape is dominated by deciduous and evergreen broad-leaved and needle-leaved
species, leaf type and phenology represent key environmental descriptors alongside
vegetation cover and height. The use of dense time-series such as those acquired by
the Sentinel-1 enables seasonal variability to be identied, particularly leaf-on and leaf-o
vegetation dynamics to be discerned (Lucas et al., 2011). In addition, temporal water
dynamics are important in Wales and the broader UK due to the impacts of ooding and
16 C. J. OWERS ET AL.
severe storms. Environmental descriptors relevant to water state and water persistence in
the landscape should be prioritised. Beyond these priorities, descriptors of lesser priority
include those relevant to non-vegetated terrestrial landscapes because of the general
absence of naturally bare areas, as well as a small proportional change in articial surface
cover over annual time scales.
A recent national voluntary review on SDGs by the UK Oce of National Statistics
(ONS) identied a major opportunity for increasing geographical disaggregation for
SDG indicator reporting (i.e. SDG indicator reporting at local, regional and devolved
levels, such as Wales) (HM Government, 2019). This gives greater granularity to identify
progress in SDG targets, however, it also requires greater capacity to standardise and
collate information. The ONS indicates several current examples being explored with
the Ordnance Survey, including 9.1.1 (Proportion of the rural population that is living
within two kilometres of an all-season road) and 11.3.1 (Ratio of land consumption rate
to population growth rate). Several SDGs indicators that have been shown to have direct
applicability to reporting by EO data are still under exploratory processes for determin-
ing appropriate spatial and temporal resolution of available data sources. However, the
LCCS would provide direct metrics to report on particular indicators, including 15.3.1
(Proportion of land that is degraded over total land area), 6.6.1 (Change in water-use
eciency over time). Several other indicators applicable for EO are already reported on
using global or Europe-wide products although these could also be reported on
through Living Earth, potentially at higher spatial resolutions and temporal frequencies
(e.g. 15.4.2 Mountain Green Cover Index or 15.1.1, Forest area as a proportion of total
land area; HM Government, 2019).
4.5. Living Earth supporting SDG reporting for any nation
Earth observation in support of SDGs (EO4SDGs) has been highlighted by several
authors recently (Anderson, Ryan, Sonntag, Kavvada, & Friedl, 2017; Avtar, Aggarwal,
Kharrazi, Kumar, & Kurniawan, 2020; Dong, Metternicht, Hostert, Fensholt, &
Chowdhury, 2019; Scott & Rajabifard, 2017), with at least 29 key indicators able to be
reported on directly through EO or indirectly as a supporting measure. Land cover is
suggested to be required in some capacity by 31 indicators (Anderson et al., 2017). Of
these, 29 enable direct input from EO for their computation, including 6.6.1
(Percentage of change in the extent of water-related ecosystems, 15.1.1 (Forest area
as a percentage of total land area), 15.2.1 (Forest cover under sustainable forest
management), 15.2.2 (Net permanent forest loss), and 15.3.1 (Percentage of land that
is degraded over total land area) (Anderson et al., 2017). All 29 EO-applicable indicators
rely on environmental descriptors (Masó, Serral, Domingo-Marimon, & Zabala, 2019)
but need to be routinely retrievable, freely available, and comparable over space and
time for global reporting strategies (Scott & Rajabifard, 2017). To achieve sustainable
development goals through consistent reporting of indicators), we suggest that Living
Earth provides a viable and potentially optimal platform for global adoption for
EO4SDGs. Living Earth ensures a transparent methodology that allows monitoring to
be standardised for all countries with the cooperation of the scientic and political
communities – key conclusions from recent reviews (Anderson et al., 2017; Avtar et al.,
2020).
BIG EARTH DATA 17
Living Earth is highly compatible and complementary with free and open access ARD
to provide standardised methods for assessing land cover and land cover change (Dong
et al., 2019). The Living Earth software package pairs with readily accessible global EO data
and current and emerging national infrastructures for EO monitoring. Apart from the
aforementioned examples of Australia and Wales (UK), the continuing development and
momentum of the Open Data Cube (ODC) provides an excellent integration with the
Living Earth software for generating land cover and land cover change products at
relevant spatial and temporal resolutions for measuring and reporting on SDGs. The
seamless integration with ODC means that Living Earth can be adopted by any nation
utilising ODC for spatial data management and analysis.
5. Conclusion
This study presents Living Earth, an implemented, exible, optimised FAO LCCS-2, suitable
for the classication of land cover in support of SDG target setting and reporting. Living
Earth provides a framework for standardised, globally applicable and comparable land
cover classication to support EO4SDGs, providing information and knowledge for action,
rather than only ARD. We resolve several semantic challenges of LCCS for consistent EO
implementation, including modications to environmental descriptors, inter-dependency
within the modular-hierarchical framework, and increased exibility associated with
limited data availability. The Living Earth software package was developed to align with
FAIR principles of software and data dissemination, as an open-source system intended to
utilise free and available EO data. The plugin design of Living Earth allows easy addition of
landscape descriptors pertinent to the use case selected. Key environmental descriptors
provide specic guidance for users and nations as a pathway for application to build
a successful ongoing and relevant national land cover product.
Acknowledgments
The authors would like to recognise the considerable eort of the Open Data Cube community to
provide an analytical framework for scalable land cover mapping. Ben Lewis, Gabrielle Hunt, Cate
Kooymans, Sebastien Chognard and Sophia German are thanked for their helpful contributions and
thoughts to implementing Living Earth from FAO LCCS-2. This paper was published with the
permission of the CEO, Geoscience Australia.
Disclosure statement
No potential conict of interest was reported by the author(s).
Funding
This research has been conducted with the support of Geoscience Australia, through the DEA Land
Cover project, and the European Research Development Fund (ERDF) Sêr Cymru II program award
(80761-AU-108; Living Wales).
18 C. J. OWERS ET AL.
Notes on contributor
Christopher J. Owers is part of the Quantitative Biodiversity Assessment team at
CSIRO exploring habitat condition and environmental change at national and
global scales. His work involves developing transformative technology to enable
rapid response to ecosystem change for more eective and ecient biodiversity
management. Previously he was part the Earth Observation and Ecosystem
Dynamics research group at Aberystwyth University, contributing expertise on
remote sensing and landscape geomorphology with an emphasis on land
monitoring and management. His research interests span environmental science
with a passion on using spatial information to identify landscape change.
ORCID
Christopher J. Owers http://orcid.org/0000-0002-7071-6667
Data availability statement
The Living Earth system is under Apache 2.0 license, available on bitbucket (https://bitbucket.org/
au-eoed/livingearth_lccs). Extensive documentation, such as detailed descriptions of FAO LCCS-2
class modications for EO, is available in the repository. All 573,307 unique landscape descriptor
codes and descriptions are provided in supplementary material along with variable importance
scores for each environmental descriptor. Any further requests should be made to the correspond-
ing author.
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