ARIES for PEOPLE-EA Explorer
The ARIES for PEOPLE_EA Explorer is a web-based application built on the k.LAB Integrated Modelling Platform. The application has access to all information (data and models) available on the Integrated Modelling network, and provides a dedicated user interface to allow the pilot countries to easily access and test the output of the PEOPLE-EA project, funded by the European Space Agency, and developed by the ARIES team (BC3) in collaboration with the VITO team and using the OpenEO (Open Earth Observations) platform.
Table of Contents
How to access the application
List of contents of the guide
1. How to access ARIES for PEOPLE-EA via a web-browser
Once registered, following the instruction in the page above, use this link:
https://peopleea.integratedmodelling.org/modeler/
?
app=aries.peopleea.enSpatial and temporal context of the analysis
At the top of the menu on the left side of the screen, you can specify the geographic area and temporal and spatial scale.
Where
At the top of the panel, there a drop-down menu provides three options to select an analysis context by zooming and panning on the map. When the "administrative regions" or "river basin" option is chosen, the currently highlighted context will be outlined in light blue.
These names are queried from the OpenStreetMap (OSM) database. Users should be aware that OSM boundaries may differ slightly from those selected using the "Administrative regions" option of the drop-down menu (i.e. a context selected with this option include the country's territorial waters)
Spatial resolution
The user can select the spatial resolution for analysis, and choose between meters or kilometers.
In case the resolution set is higher than the available input data, ARIES will compile accounts at the selected resolution, but based on the finest grained available data.
When
Select the year(s) for your analysis. If data are missing for a specific year of interest, nad the option of temporal mediation is enabled, ARIES automatically fills the gap using the closest available year's data.By default, the context is set to compute results for 2018, which corresponds to the latest year the Corine land cover dataset, this being one of the most important input of the output produced in the application. The user cna observe result for a single year, or for a longer period of time: I. Single-year analysis (the box in this section is unchecked) and select the year of interest
II. Multi-year analysis - to show change over a longer period of time check the box and select the first and last year of the period to analyze.
Reset
The red "X" button on the upper right can be used to reset a previously selected context, or to stop a computation in progress (all computed results will be lost).
Forest ecosystem condition accounts
The Ecosystem Conditions accounts are composed of 3 main outputs:
- Variable (descriptor) = the original ecological measurement (raw variable) used to study a certain aspect of condition;
- Indicator = the rescaled ecological measurement (raw variable) to allow a consistent representation of the dimensions of the conditions captured in the variable. Reference values are used to determine good and bad values for a particular variable in the same type of forest (aka forest with similar characteristics), for the sake of comparing those results against other dimensions of condition in the same type of forest, or against results for the same variable in a different area.
- Index = the composite weighted average of a set of indicators, whose goal is to combine different information from several variables into a unique figure, representative of the overall condition of that type of forest in the area under analysis.
Break-down (categorization) of the results by European Forest
In this analysis, being forests the main objective of this study, the conditions of forests are disaggregated into the classes of forests obtained combininig the information described in this chapter of the guide.
Forest types
European forests have been initially categorized based on their landcover forest class, as per the Corine dataset, which distinguishes 4 types of forest:
- Broad-leave forest (311) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-311.html
- Coniferous forest (312) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-312.html
- Mixed forest (313) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-313.html
- Transitional woodland & shrub (324) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-324.html
Bioregion zones
Such categories of forest were also combined with the information on the European bioregion zones, used to define 11 bioregions over:
- Alpine,
- Arctic,
- Atlantic,
- Black Sea,
- Boreal,
- Continental,
- Macaronesian,
- Mediterranean,
- Pannonian,
- Steppic regions
- Alpine (Scandinavia)
The combination of landcover forests and bioregions results in 44 forest types, following the definition of Maes, J., Bruzón, A.G., Barredo, J.I. et al. Accounting for forest condition in Europe based on an international statistical standard. Nat Commun 14, 3723 (2023).- https://doi.org/10.1038/s41467-023-39434-0
The following datsets were used to obtain this information:
Forest ecosystem condition variables
The table below summarizes all the ecosystem condition metrics considered in the development of the Forest Conditions Accounts
The full list is still under development
A. Abiotic ecosystem characteristics
A.1 Physical state
Physical descriptors of the abiotic components of the ecosystem (e.g. Soil structure, impervious surface, water availability)
A1.1 Normalized Difference Water Index
GEE Landsat 8 Collection 1 Tier 1 32-Day NDWI Composite
These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the computed top-of-atmosphere (TOA) reflectance. See Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive to changes in liquid water content of vegetation canopies. It is derived from the Near-IR band and a second IR band, ≈1.24μm when available and the nearest available IR band otherwise. It ranges in value from -1.0 to 1.0. See Gao (1996) for details.
These composites are created from all the scenes in each 32-day period beginning from the first day of the year and continuing to the 352nd day of the year. The last composite of the year, beginning on day 353, will overlap the first composite of the following year by 20 days. All the images from each 32-day period are included in the composite, with the most recent pixel as the composite value.
2013-2022
A.2 Chemical state
Chemical composition of the abiotic ecosystem compartments (e.g. Soil nutrient concentration, air and water quality)
A2.1 Soil Organic Carbon
2003 OCTOP: Topsoil Organic Carbon Content for Europe - Organic carbon content in the first 30 cm of soil
2014 LUCAS: Topsoil Soil Organic Carbon (LUCAS) for EU25 - Organic carbon content in the first 30 cm of soil
B. Biotic ecosystem characteristics
B.1 Compositional state
Composition/diversity of the ecological communities at a given location and time (e.g. Presence/abundance of key species, species richness, genetic diversity, presence of threatened species, diversity/abundance of relevant species groups)
B1.1 Threatened Forest Bird Species diversity
B.2 Structural state
Aggregate properties (e.g. mass, density) of the whole ecosystem or its main biotic components (e.g. Total biomass, canopy coverage, annual maximum NDVI, Vegetation density, habitat structure, food chain and trophic levels)
B2.1 Above-ground biomass
ESA’s Climate Change Initiative Biomass project provides global maps of above-ground biomass (Mg ha-1), with these being capable of supporting quantification of biomass change.
B2.2 Leaf Area Index
The LAI quantifies the thickness of the vegetation cover and it's recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS).
B2.3 Tree cover density
It offers high-resolution information on the percentage of tree cover in a given area
B.3 Functional state
Summary statistics (e.g. frequency, intensity) of the biological, chemical, and physical interactions between the main ecosystem compartments (e.g. Primary productivity, community age, distribution frequency, decomposition processes)
B3.1 Net Primary Production
Dry Matter Productivity and Net Primary Production
The ecosystem Net Primary Production (NPP) represents the net growth rate of the vegetation(excluding autotrophic respiration), expressed in kilograms of production per hectare per day (gC/m²/day). It is a proportion of Gross Primary Production and it's directly related to ecosystem Dry Matter Productivity, expressed in kgNPP/ha/day.
B3.2 Fraction of Photosynthetically active radiation
https://docs.terrascope.be/DataProducts/Sentinel-2/references/VITO_S2_ATBD_S2_NDVI_BIOPAR_V200.pdf
The FPAR (Fraction of Photosynthetically active radiation) quantifies the fraction of the solar radiation absorbed by live leaves for the photosynthesis activity. Then, it refers only to the green and alive elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical properties, atmospheric conditions, and angular configuration. To overcome this latter dependency, a daily integrated FAPAR value is assessed.
B3.3 Burned severity
MOSEV: A global burn severity database from MODIS (2000-2020)
- Global Patterns and Dynamics of Burned Area and Burn Severity (developed from MOSEV data)The variable is a result of the reprocessing of the relativized version of the Normalized Burn Ratio (RdNBR) spectral index. The 8-daily values of the original dataset, RdNBR, were converted into a burn severity category (non-burnt, low, moderate-low, moderate-high, high) and annualized by selecting the most represented category over the period observed.
2000-2020
B3.4 Drought Severity
https://crudata.uea.ac.uk/cru/data/drought/#global
Self-calibrating Palmer Drought Severity Index (scPDSI): The scPDSI metric was introduced by Wells et al. (2004), who gave detailed information about its calculation. The scPDSI is a variant of the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from a time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location.B3.4 Drought Severity (2nd variable - alternative)
Combined drought indicator (v3) - A combination of spatial patterns of precipitation, soil moisture, and greenness vegetation anomalies, the CDI identifies areas at risk of agricultural drought.
2012-2023
B3.5 Normalized Difference Vegetation Index
https://land.copernicus.eu/global/products/ndvi
The Normalized Difference Vegetation Index (NDVI) is an indicator of the greenness of the biomes.
2020 to present
1998 to 2020
B3.6 Green index
B3.7 Fire occurrence
C. Landscape level characteristics
C.1 Landscape and seascape at coarse scale
Metrics describing mosaics of ecosystem types at coarse (such as landscape and seascape) spatial scale (e.g. Landscape diversity, connectivity, fragmentation, ecosystem type mosaics)
C1.1 Forest Connectivity
Generated using GUIDOS toolbox and Corine landcover daatset - Methodology
C1.2 Landscape Naturalness
Generated using GUIDOS toolbox and Corine landcover daatset - Methodology
C1.1 Forest Fragmentation
Relative Magnitude of Fragmentation (RMF)
Global remote-sensing data product (i.e. the 27-year annual ESA CCI land cover maps which can be categorized as an EBV ‘Ecosystem Distribution’)
Forest ecosystem condition indicators
Each variable is scaled between 0 and 1, with values getting closer to 1 as the forest conditions improve.This allows each indicator to represent in a consistent way the information coming from the variables, which in their original values have different ranges but also different interpretation of such values, and not always higher value are associated to better conditions (e.g. if we look at variable like Drought Severity or Forest Fragmentation, the higher the value, the worse the forests conditions).
There are currently 10 indicators computed in the the application.
These are ordered by the their ECT classification:
A1.1 Normalized Difference Water Index
A2.1 Soil Organic Carbon
B1.1 Threatened Forest Bird Species Diversity
B2.1 Above-ground Biomass
B2.3 Tree Cover Density
B3.1 Net Primary Production
B3.5 Normalized Difference Vegetation Index
C1.1 Forest Connectivity
| Note |
|---|
| There are two methodologies adopted to define the Condition Index Accounts, both follows the theoretical framework developed in the scientific publication Maes, J., Bruzón, A.G., Barredo, J.I. et al. Accounting for forest condition in Europe based on an international statistical standard. Nat Commun 14, 3723 (2023), here in after referred to as the Nature Communication method. The differences in their implementation (and their reasons) are fully documented in this guide. |
Reference areas representing healthy forests
These areas are used to identify forests considered in good health, to define the upper reference values of that variable. Forests considered in good condition (the upper reference areas) must meet the two following criteria:
- being primary forest, alias forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management or
- being protected areas (form before the year 2000) and highly tree-covered throughout the time series (areas with a tree-covered density loss equal to or higher than 5% are excluded).
Such criteria are observed based on the information of the following datasets:
- Primary forests from EPFD v2.0 - https://www.nature.com/articles/s41597-021-00988-7
- Protected Areas from IUCN data - https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA
- Copernicus's High-Resolution Layer Tree Cover Density - https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density
Results are obtained by combining the following inputs by using, as consistently as possible, the year 2000 as the reference year, except for the information on the Tree-cover density, whose earliest observation is in the year 2012.
Whenever a forest fails to meet any of the listed criteria, it is not considered an area of reference for good condition
Primary forest in the PeopleEA approach:
Primary forests were identified by taking:
- the polygons of primary forests,
- the points of primary forests and buffer to 200m radius.
Primary forests extents match roughly with the Maes et al. figures (Supplementary Table 8)
Protected Area in the PeopleEA approach:
The Protected Areas that have been protected areas from before the year 2000 and which don't exhibit tree-cover loss higher than 5% were considered for this analysis.
The categories of protected areas considered were:
- Strict nature reserves 1a,
- Wilderness or wildlands 1b, and
- National parks 2.
| Note |
|---|
In the Maes approach, depending on the bioregion analysed, different categories of Protected Areas were selected. |
| Warning |
|---|
Healthy reference areas roughly match, but there are significant differences (>5%) for some European forest categories. |
- The upper (healthier conditions) reference threshold was set at the 98th percentile value observed in the reference healthy areas, for each type of forest, for each variable.
- The lower (worse conditions) reference threshold was set at the 2nd percentile value observed in the rest of the forest area
(Total forest area - healthy reference area).
- The percentile might be inverted in case the healthier conditions correspond to lower values.
Rescaling of the variables
The percentile computed define the thresholds used to rescale the observed variables values into indicators ranging from 0 to 1.
Forest ecosystem condition indices
The Forest Condition Index quantifies forest health by summarizing the information of several indicator accounts, each representing a different aspect of the forest conditions, thus is computed by taking into account the several dimensions represented by each metric (raw variable).The indicators composing the index aim to represent comprehensively all the components of ecosystems (abiotic, biotic and landscape). The index is usually composed of a single indicator for each Ecosystem Typology Class (ETC), since the variables belonging to the same ETC are usually highly correlated. The indicators are selected considering their relevance, their direct relationship to forest conditions and the availability of data for their measurement.
Forest Condition Index in the People EA methodology
The index built in the People EA project is the result of the weighted average of these indicators:
- the Net Difference Water Index (NDWI),
- Soil Organic Carbon (SOC),
- Threatened forest bird species diversity (TFBSD),
- Above-Ground Biomass (AGB),
- Net Primary Production (NPP)
- and Forest connectivity (FC).
The weights for indicator accounts are determined according to:
- their spatial resolution (i.e. the size of the smallest feature detected by a satellite sensor or displayed in a satellite image, usually expressed as a single value measuring the length of one side of a square),
- temporal resolution (i.e. the total amount of years in a dataset),
- temporal frequency (i.e. the availability of temporal observations over the same period) and
- dataset quality (referring to the proximity of the latest year to the present).
In particular, indicator accounts are ranked according to each of these categories. Larger values of these criteria are associated with a better representation of the ecological conditions and, rank in a higher position. The relative sum of the indicator accounts positions results in their index weight. When data from more than one indicator is considered equal in one particular aspect, each indicator is attributed to the average of the positions they would represent. For example, if three indicator accounts should be ranked 7, they will be assigned 6 since it is the average of the positions these variables represent (5th, 6th and 7th). Ranking and weights attributed to indicator accounts are detailed in the table below:
The index has been designed to take into account one indicator for each Ecosystem Typology Class.#
ETC class
Raw Variable
Temporal resolution
Temporal frequency
Dataset quality
Total
Final Weight
1.0
The table ranks each variable considering the aspects in the column headers.
- Spatial Resolution describes the size of the smallest feature that can be detected by a satellite sensor or displayed in a satellite image. It is usually presented as a single value representing the length of one side of a square,
- Temporal resolution refers to the total amount of years in the dataset,
- Temporal frequency considers the availability of temporal observation over the same period. While all variable are annualized, annual averages obtained from highly frequent observation are more accurate and thus considered better input overall.
- Dataset quality represents the proximity of the first and latest year in the dataset, respectively, to the to reference year (2000) and to the present.
- When variables are ranked the same in one criterion, they are assigned the average of the positions they would represent. For example, if there are three variables which should be ranked 7, they will be assigned 6 since it is the average of the positions these variables represent (5, 6 and 7).
Indicators weighted using a weighted arithmetic average (PeopleEA index)
Overall condition index =
(Net Difference Water Index x 0.24) + (Soil Organic Carbon x 0.05) + (Threatened Forest Bird Species Diversity x 0.08)+
(Above-ground Biomass x 0.15) + (Net Primary Production x 0.19) + (Burned severity x 0.17) + (Forest Connectivity x 0.12)
The formulation of the resulting Forest Condition Index is the following:
Forest Condition Index = sqrt(
(NDWI^2 x 0.27) + (SOC^2 x 0.07) + (TBFSD^2 x 0.10) + (AGB^2 x 0.18) + (NPP^2 x 0.22) + (FC^2 x 0.16) )
For areas where Soil Organic Carbon is missing, because of dataset coverage, the index is calculated by redistributing the weights:
Forest Condition Index = sqrt(
(NDWI^2 x 0.31) + (TBFSD^2 x 0.11) + (AGB^2 x 0.19) + (NPP^2 x 0.24) + (FC^2 x 0.15) )
Forest Condition Index published in Nature Communications by Maes et al. (2023)
The Forest Condition Index quantifies forest health by summarizing the information of several indicator accounts, each representing a different aspect of the forest conditions. The indicators composing the index aim to represent comprehensively all the components of ecosystems (abiotic, biotic and landscape). The index is usually composed of a single indicator for each Ecosystem Typology Class (ETC), since the variables belonging to the same ETC are usually highly correlated. The indicators are selected considering their relevance, their direct relationship to forest conditions and the availability of data for their measurement.
Specifically, the Forest Condition Index published in Nature Communications considers:
- the Net Difference Water Index,
- Soil Organic Carbon,
- Threatened forest bird species diversity,
- Tree cover density ,
- Net Difference Vegetation Index (Forest productivity),
- Forest connectivity and
- Landscape naturalness.
To obtain a unique assessment of the forest condition, the index uses the weighted average of indicator accounts. The weights for indicator accounts are assigned based on their capacity to represent the ecosystem structure (intrinsic relevance), the ecosystem services provided (instrumental relevance), the changes in forest condition derived from external factors (directionality meaning) and the anthropogenic repercussion (sensibility to human influence) as well as on their adequacy to the SEEA EA framework (framework adequacy). Indicators are ranked between 1 (lowest position) and 7 (highest position) for five conceptual criteria proposed to select ecosystem condition variables (SEEAEAWhiteCover2021, CzuczEtAl2021 . Better positioned indicators have a larger weight in the index, and the relative sum of the indicator positions results in their index weight. Positions and weights attributed to indicator accounts are detailed in the table below, which corresponds to Maes et al. figures (Supplementary Table 9).
#
ETC class
Variable
Instrumental relevance
Directional meaning
Sensibility to human influence
Framework conformity
Total
Final Weight
1.0
Forest Condition Index =
(NDWI x 0.08) + (SOC x 0.12) + (TBFSD x 0.22) + (TCD x 0.21) + (NDVI x 0.13) + (FC x 0.13) + (LN x 0.11)
For areas where Soil Organic Carbon is missing, because of dataset coverage, the index is calculated by redistributing the weights:
Forest Condition Index =
(NDWI x 0.10) + (TBFSD x 0.26) + (TCD x 0.23) + (NDVI x 0.15) + (FC x 0.14) + (LN x 0.12)
Compile the accounts in the k.Explorer
To trigger the computation of an output in a specific area, for a specific period of time, the user should first select context has been set, the user simply checks the box of the condition metrics (raw variables, indicators or index) or the ecosystem service to compute their results.When the knowledge bar turn yellow and the elephant's ball spins, the system is computing the information queried.
There are several sections to organize the access to the different information made available in the application:
The first 4 sections were specifically developed for the People EA project, and will be the main focus of this guide.
When the 3 dots are displayed horizontally, it means that the options in the menu are hidden 
By clicking on the 3 dots they get disposed vertically 
and a drop-down menu lists the options available.
The main output of an observation can be a table
, or a map 
Before computing any of these results, the guide provides the theoretical background followed to obtain these output in the previous section, so users are invited to carefully read that documentation in order to be able to select the options of calculations available in the app, as well as to interpret correctly their results.
Users can observe:
Forest Condition Indexes
the first option is to compile the Forest Condition Index to estimate the overall condition based on a set of experts-defined weighted and selected indicators
The symbols next to the Indexes define the output produced as result of that computation, while the fist Condition Index produces maps and a table, the other two indexes produce maps, and can be compared to show how conditions estimate differ when using diverse methodologies.
| title | Obtain summary tables for the indicators and variables |
|---|
Forest Condition Variable (raw values)
within the section of the Condition metrics, the value of the Raw variables to compile the Variable Forest Ecosystem Condition Account(s),
Forest Condition Indicators
switch to the Indicators option, to compile the Forest Condition Indicators Account(s),
High resolution Forest Condition Variable
These variables are placed in another section to differentiate them from the other raw variables.| Warning |
|---|
| High resolution observation should only be done over small context defined as testing areas in the project. |
This is due to the computational cost and time associated to these observations, which is much higher.
The selection of one of these options triggers the model(s) underlying the output(s) selected, in case more ecological metrics are selected, the computational flow is continuous. When the whole workflow to compute one metric is completed, the system moves to the next tasks in the queue of computation. The models to be computed follow the order of selection of the user.
Most of the metrics are modeled from different datasets, so time of computation should be similar, but in case an account shares inputs used in a previous computation, those are not re-calculated, thus one should expect the additional output(s) to be computed faster.
Soil Retention Ecosystem Service
As a demonstration of the interoperability between ARIES and OpenEO systems, the Soil erosion model can be computed in two different ways:using the dynamic C-Factor from Open EO → select the OpenEO Cover Management and only after select the Sediment Regulation model:
select the OpenEO Cover Management
Once obtained the Cover Management, call the Sediment Regulation model
Once obtained the Cover Management, call the Sediment Regulation model
The breakdown of the results focuses on the contribution of the service by type of forest
In this case, the static C-Factor is used in the workflow
And the Soil Retention results are comparable but have a coarser resolution
The breakdown of the results focuses on the contribution of the service by type of forest
The output of the ES are the same
but the breakdown of the results focuses on the contribution of each Ecosystem Type
| title | Observation are live digital twins |
|---|
Any observation done in ARIES happens on the fly, is not a predefined combination or workflow, of models and data. The system builds the most appropriate strategy to answer such question using the information available in the system, in the moment in which a query is made. This allows to a model to improve over time as new information is integrated in ARIES.
ARIES would usually look for the "best" available combination of data and models to estimate Soil retention. By selecting the input (C-Factor from OpenEO) and later the model to be computed, we steer the system to build an observation of Soil Retention using the Cover Management dataset previously selected.| Tip | ||
|---|---|---|
| ||
A legitimate question, and the answer is that it would be prioritized if it was publicly available, because of the highest temporal and spatial resolution of the data. |
Results of the accounts
Once the account is computed, the k.Explorer moves automatically from the Data view, to the Documentation section, showing the Table section (for more information on the different sections check this chapter of the guide)
The table can be copied or downloaded (click on the symbols at the right bottom of the table to export it)
To explore the geospatial explicit information in the maps used to summarize the results in the table, go back to the View Data section:
Expand the View Tree to visualize all inputs, intermediate and final output
Select the map that you want to visualize
When the knowledge bar turn yellow and the elephant's ball spins, the system is computing the information queried.
In this case, is loading the map
This can be visualized in the explorer or downloaded as a raster file (.tiff format) for further analyisis in a GIS system.
One can download a map by clicking on the arrow pointing down (↓) that appears when hovering over the observation ( an observation is any of the element observed in the workflow and listed in this menu)
This was the main output, but any input and intermediate output of a workflow can be observed by ticking their boxes.
The results for NDVI are shown below.
Notice how observations that changes over time in the context selected, have the symbol of a clock next to them, and at the bottom of the menu, you'll see a timeline, in a light blue color.
In this example, as there are just two temporal observations, there is just one separator (small tick in yellow), dividing the 2 temporal observations (2015 and 2016).
Selecting a different temporal observation, the map will change and the system displays the result for that year
2015
2016
2. Set the context of your analysis
3. Forest ecosystem condition accounts
4. How can accounts be compiled on the selected context?
4.1 Extent Accounts
4.2 Condition Accounts
4.3 Service Accounts
5. How to visualize and download the accounts, compare the results against other frameworks, as well as add new data and models
6. Other quick tips
ARIES for SEEA accounting tables
Below the Forest Condition Index option, there is a section containing the results of the ARIES for SEEA application.
Each account contains a drop-down menu (three horizontal dots), from which the user can select accounts to compile:
Extent Accounts
These accounts measure the extent of the IUCN Global Ecosystem Typology ecosystem types, or land cover, present in the context of your analysis, in km2.The different types of accounts provide varying levels of detail in summarizing ecosystem/land cover extent and its change over the selected time period.
Condition Accounts
These accounts measure ecosystem condition. Currently, only forest ecosystem condition accounts are supported, but condition accounts for other ecosystem types will be added soon (beginning with those for grasslands).
The conditions metrics available for inclusion in the account appear in a drop-down menu when the user clicks on the triangle next to "Forest condition metrics".
Three types of ecosystem condition accounts are available:
1. Condition Variable Account: report the value of each condition metric in their originally observed values (non-transformed);
2. Condition Indicator Account: rescale ecosystem condition variables to values between 0 and 1. Rescaling is calculated as the difference between the observed condition variable value and the optimal condition reference.
By normalizing multiple condition variables, different indicators can be more directly compared;
3. Condition Index Account: combines all indicators together using a weighted mean. Currently, all indicators take the same weight, summing to 1 (e.g., 0.25 when four condition metrics are selected). In future releases of ARIES for SEEA, users will be able to assign custom weights to the indicators to better reflect their local importance when accounting for ecosystem condition.
Ecosystem services account (physical terms)
These accounts measure the biophysical quantities of services provided by ecosystems and used by economic units. Use tables are not explicitly supplied with the model outputs, but use is described in the automatically generated reports for the selected accounts. In the current version, four ecosystem services are available. A fifth one, Nature-based tourism, is in its final stages of development and will be made available in a future ARIES for SEEA release.
Ecosystem services account (monetary terms)
These accounts measure the monetary value of the selected ecosystem services, applying SEEA EA-compliant valuation method(s). Use tables are not explicitly supplied with the model outputs, but use is described in the automatically generated reports for the selected accounts. In the current version, three ecosystem services are available. A fifth one, Nature-based tourism, is in its final stages of development and will be made available in a future ARIES for SEEA release.
Spatial and temporal aggregation
The user can select how the results of the accounts are aggregated in accounting tables.Currently, only the first option is available. In future ARIES for SEEA releases, the user will be able to generate accounting tables as follows:
Temporal accounting
When a multiple-year analysis is selected, the user can decide to output tables:
- For the first and last years of the time series only, or
- For all years within the time period selected.
Key outputs and observations
This section shows the most relevant spatial inputs and outputs in the account(s) run by the user
(e.g., in the case of ecosystem extent, the most relevant outputs are land cover and ecosystem type data).
In the upper left corner of the application, there is a section dedicated to SEEA-relevant indicators.
This section includes selected indicators from the Sustainable Development Goals (SDGs) and the Convention on Biology Diversity (CBD) Post-2020 Biodiversity Indicators, which have been added to the application.
Once it is opened, a drop-down menu shows the list of available indicators.
Those selected by the user are added in the ARIES for SEEA panel as if they were an additional standard SEEA EA account
Quick tips
Set your context (e.g., Cape Town in South Africa). Suggested way:
will show the entire context as currently selected:
To do so, point the cursor over the search bar, and once this symbol
appears, drag it to a preferred location.
Explore results and methods used to run your accounting models
Aspects of the user interface change once you begin to run an accounting model:
Run an accounting model (e.g., ecosystem extent account - simple net balance).A rotating gear will appear next to the selected account(s), which indicates that the model is being compiled.
When a model input or intermediate output has been computed, it is listed and can be explored individually, by selecting the specific layer (e.g., aridity):
In the right-upper corner of the app, three tabs are now available:
In our example, the application automatically takes the user to the section showing the accounting result(s).
This happens every time a final outcome is generated:
To review other sections of the documentation:
- A general introduction to the model(s);
- Information on the SEEA framework or any other more general modeling frameworks (when part of a larger set of models);
- The methods applied;
- A summary of the main results;
- Caveats or other considerations in interpreting model results, as part of the discussion; and
- Reference(s) for data and method(s) used.
Tabular results
To ease the observation of the results:
Image(s) section
Figure(s) and (maps) generated during the computation are listed in this section and can be explored individually.If results come from multi-year models, the maps are interactive and show changes over time.
Resources section
Lists all the resources used in the computations, to provide full-transparency on the final output.Each of the data resource can be explored individually
Dataflow section: shows a list containing a visual summary of each model component and how these were combined to obtain the final results. In the next version of ARIES for SEEA, this section will also report the decisions taken by the system on:
The third tab available once a model has run is the Dataflow view
This shows a diagram that visually summarize each model component and how those were combined to obtain the final result(s). Clicking on an individual rectangle provides details about the dataset, algorithm, etc. used at each step of the model.
Other ARIES features
While most of the functions needed to compile SEEA accounts are available through ARIES for SEEA interface, the normal functionalities of the ARIES Explorer remain available through the search bar.
This sections shows how to query them.
Search the knowledge bar (1)
This box is used to call models, select your context, modify default settings, and show the help page.
Query an observable from the space bar (A):
Please note: unlike the search bar in the ARIES for SEEA application, the general ARIES Explorer search bar is case-sensitive.
Press the space bar on the empty box to receive suggestions.
Start with an upper-case letter to search for a geographical context (invalid if you have already set a context).
Start with a lower-case letter to search for an observation (e.g., elevation),
Option (iii) enables users to compute a model available in the ARIES Explorer environment and appropriate for the SEEA framework, but which is not contained as an option in the application.
This option can be used to explore a wide range of different models (termed observables), which can refer to model inputs or outputs.
Context:
- Reset a previously used context
- Draw a new context (using your cursor)
- Adjust the spatial and/or temporal resolution of your analysis
- Activate the interactive mode to add manually add input to the model (adjusting parameter values)
- Remember location (remember the last context selected)
- Remember docked status
New option to show the coordinates of your cursor (they change as you move it around)
Zoom (2)
Zoom in (by clicking on the 
) and out (
). This can be useful when selecting your context using map boundaries (entire rectangle shown in the interface) or administrative regions or river basins, which change as a user zooms and pans the map.
Background map (3)
Select the background map you prefer. By default, ARIES uses OpenStreetMap as it currently offers the best user experience even at higher resolutions.
The user can switch to other available background maps as desired.