Green Score Capital, a startup created in October 2019 and supported by the European Space Agency, is developing an expert system assessing the biodiversity and carbon footprints of their customers' activities and products by combining their own data with external data. The biodiversity impact of an activity is directly related to its location on the globe and the characteristics of the local habitats. In this presentation, we'll describe how we use FME to compute several indicators that are directly relevant to the evaluation of the footprint on biodiversity. This computation leverages the WorldCover dataset, a worldwide land cover dataset funded by ESA and based on Copernicus Sentinel-1 and Sentinel-2 data. It also uses the ""Landscape Metrics"" R package through the RCaller transformer. Finally, we'll show how we are able to automatize the process on a large scale using an Automation on an ""Entreprise"" FME Cloud instance.

1. Using FME Flow Hosted, space data
and R libraries to help assess
production impact on environment
Ellipso Facto / Veremes
Green Score Capital

2. Presenters
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Frédéric
Eichelbrenner
FME Addict
Veremes/Ellipso Facto
Valérie
Tiersen
CEO and founder
Green Score Capital

3. The
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Agenda
● Why focusing on environment ?
● Evaluating biome characteristics
● Relevant datasets
● FME process

4. The
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Introduction

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“There is now unequivocal evidence that
biodiversity loss reduces the e ciency by
which ecological communities capture
biologically essential resources, produce
biomass, decompose and recycle
biologically essential nutrients.”
Cardinale, B.J. et al. (2012) Biodiversity Loss and its
impact on humanity, Nature News.
Available at: https://www.nature.com/articles/nature11148

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Why focus on biodiversity here ?
Most alerts relate to climate change and carbon emissions
Biodiversity is a climate stabilizing factor
Biodiversity is central in coming regulations

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• Startup created in 2019 by Valérie Tiersen
• Help measure carbon and biodiversity footprint
• Supported by European Spatial Agency and CNES
• Awarded "1000+ profitable solutions to protect the
environment" by the Solar Impulse Foundation in July 2023

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Current project objectives
• Assess impact of companies activity on environment
• Based on “connectivity scores” upon production areas

9. Project Team
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Eliette
Ascensio
Research Engineer
Morgane
Faucheux
GIS Specialist
Lucille
Marescot
Phd
Scientific Consultant

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Habitat connectivity
How and to what degree distinct patches of habitat are
connected, which can influence the distribution, genetic
diversity, and health of animal and plant populations

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Connectivity metrics
Computed using diverse mathematical
formulas depending on :
● Size
● Shape
● Spatial repartition
of spatial patches
For example
● Euclidean Nearest-Neighbor (ENN) measures the distance
between patches of the same class.
Packages available in R language

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Proof of Concept

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Input data
● Land use datasets
○ Global Land Cover (Copernicus)
■ ESA funded, 100m resolution, 21 classes, 2015 to 2019
○ World Cover (Copernicus)
■ ESA funded, 10m resolution, 11 classes, 2020 and 2021
● WWF biomes
○ Detailed forest categories
● Administrative regions

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Output data
● Individual metric values upon several years
● Global score by combining several metrics
○ for one specific area of interest
○ for many administrative regions (precomputed score)

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General algorithm
Given the area of interest
● Extract land use data
● Adapt data to computation (projection, resolution, classes, …)
● Refine land use classes with external datasets (WWF)
● Run metrics computation (using R packages)
● Combine metrics to get global score
● Publish output format (excel, database, HTML, …)

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Focus on some key technical aspects
● Extract land use data from S3 buckets
● Adapt land use classes from Copernicus to target
● Call R functions
● Deployment on FME Flow Hosted

17. The
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23 Extract land use data from S3 buckets

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23 Adapt land use classes
Copernicus 21 classes Target 7 classes

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23 Adapt land use classes : trick with FME
● Build a fake GRAY8 palette representing the mapping
● Apply this palette to the land use image
● Replace the palette with the final classes (RGB and names)
GRAY8
0 0
1 0
10 3
20 1
30 1
40 1
50 4
60 6
70 6
80 7
90 5
…
RGB24
0 0,0,0
1 255,255,76
2 0,204,0
3 100,140,0
4 250,0,0
5 0,150,160
6 180,180,180
7 0,50,200
8 255,255,255
…
STRING 40
0 inconnu
1 agriculture
2 foret tropicale
3 foret tempérée
4 urbain
5 zone humide
6 desert
7 eau
8 inconnu
…

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23 Call R functions
● Input as “data frame” structures
● All input features directly available
● No “group by”
● Output data as “data frame” rows
● Benefits from numerous packages
Similar interface as InlineQuerier

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23 Deployment on FME Flow Hosted
● Why ?
○ CPU on-demand
○ Automations
○ Cost effective
● Tricky
○ R package manual
deployment on Linux

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Results
● Automate computation on
○ 200 regions of interest
○ 3 years
● Around 30 hours of Enterprise instance

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Conclusion

24. The
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23 Key takeaways
● Environment has a huge importance for our future
● Spatial data is very helpful to assess impact on environment
○ Copernicus data widely available in several domains
● FME as a powerful tool to build ad hoc processes
○ Specially efficient for a proof-of-concept
● (FME + R) is a winner
● FME Flow offers fast and efficient deployment capabilities
○ Automations and Server/Gallery Apps are game changers

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Resources
● https://greenscorecapital.com/en/
● https://solarimpulse.com/solutions-explorer/footprint-target

26. ThankYou!
frederic@ellipsofacto.com
frederic.eichelbrenner@veremes.com