Integrated modelling combining earth observation/remote sensing, species distribution modelling, and machine learning. A good case of using open data to aid natural resources management and governance.

1. LEO KRIS MARIANO PALAO
Senior Research Associate – Geospatial Specialist
CIAT Data Intelligence Hub
l.palao@cgiar.org
International Conference on Governance and Development
Acacia Hotel Manila, Alabang, Philippines
20-21 November 2018
Data Intelligence and Governance: Earth
Observation, Open Data, and Machine Learning
for Near Real-Time Monitoring of Vulnerable
Plant Species - case of Latin America
Authors: Burra DD*, Barua MA, Palao LK, and Reymondin L.

2. CIAT RATIONALE METHODS RESULTS DISCUSSION CONCLUSION

3. CIAT RATIONALE METHODS RESULTS
Problem
• Updating of land cover map is every 5 years for official
data – only after five years we can monitor if we are
losing forest
• Mountain ecosystems are shrinking and biodiversity
within those systems are threatened
• Threats to biodiversity includes forest loss, land
development (mining), agricultural expansion, poor
governance, and pressures from population which
continue and will increase in the future
• There should be a system in place that is fast, cost-
effective, reliable, and at scale to advise on status of
forest and biodiversity that can help governments and
private sectors systematically respond to threats
Opportunity
• Big data: use and development of open, harmonized,
interoperable, integrated datasets from multiple domains
combined with Artificial Intelligence
• Terra-i is a tool developed by CIAT that monitors forest
loss in near real-time (10 days | every 2 weeks updates)
using earth observation
• We do know where are we losing forest but we don’t
know the biodiversity contained within
• We borrow the modeling framework of species
distribution that predicts where a species can thrive given
a set of environmental conditions
• Prumnopitys andina and Pilgerodendron uviferum – both
of these species are endemic temperate rain forest of
Southern Chile and Southwestern Argentina, and also
included in the IUCN red list
DISCUSSION CONCLUSION

4. CIAT RATIONALE METHODS RESULTS DISCUSSION CONCLUSION
1.)
• Identify threatened species from IUCN List
2.)
• Use rGBIF for automated querying and access of data from GBIF (largest
database globally for biodiversity)
3.)
• Use machine learning to model, predict, and map species distribution
4.)
• Use terra-i for near real-time monitoring of forest loss
5.)
• Combine species distribution and terra-i to know areas where forest loss is
observed and if the species is present

5. Remote Sensing
CIAT RATIONALE METHODS RESULTS
Integrated Modeling Approach: Combining two modeling framework to upscale biodiversity conservation, management, and
response
DISCUSSION CONCLUSION
Species
Distribution
(source: SSDM vignette CRAN-R - https://cran.r-
project.org/web/packages/SSDM/vignettes/SSDM.html)
Monitoring of
habitat loss

6. Satellite Data
RemoteSensingApproach
Time Series Collection
Machine Learning Algorithm
CIAT RATIONALE METHODS RESULTS
Machine Learning: random
forests, Xtreme gradient boosting
Decision Tree X 500
Probability
Bioclimatic Variables (worldclim.org)
SpeciesDistributionModelling
Species Occurrence of P Andina sp.
DISCUSSION CONCLUSION
Forest
Loss
Yes No
Near Real-Time Alerts (every
10 | 16 days)

7. CIAT RATIONALE METHODS RESULTS DISCUSSION CONCLUSION

8. CIAT RATIONALE METHODS RESULTS
Prumnopitys andina
Accumulated forest loss as of 15
August 2018P. andina Presence
P. andina Absence
Deforestation event
DISCUSSION CONCLUSION

9. CIAT RATIONALE METHODS RESULTS DISCUSSION
Pilgerodendron uviferum
Accumulated forest loss as of 15
August 2018P. uviferum Presence
P. uviferum Absence
Deforestation event
CONCLUSION

10. CIAT RATIONALE METHODS RESULTS DISCUSSION CONCLUSION
• Terra-i is a great tool to monitor forest ecosystems and biodiversity loss
• Given limited access to biodiversity surveys, SDM is a useful tool to estimate the location of where
vulnerable plant species can be found
• With proper communication channels, the near real-time information can be used for an early warning
advisories of forest and biodiversity status that can support actions and interventions
• Climate scenarios have not yet been taken into account. The current suitable areas might still shrink or
shift 30 or 50 years from now.
• This will have implications on available spaces to sustain their habitat -> habitats can be further
marginalized considering deforestation
• With the dynamic nature of environmental governance, such information (high frequency updates) is
essential in continuously updating the plans and programs of concerned agencies and organisations to
make it more responsive and proactive

11. CIAT RATIONALE METHODS RESULTS DISCUSSION CONCLUSION
• Near real-time threat monitoring of vulnerable species can be done combining two methodological
frameworks of SDM and Earth Observation
• With proper response from gov’t with support from private sector, the near real-time monitoring of
deforestation and species distribution modeling can help avert two things: 1) forest loss, and 2) loss of
threatened biodiversity species
• This provides yet another case of open data which is a call for action to further supporting the call for
FAIR (Findable, Accessible, Interoperable, and Reusable) data
• The methodological framework addresses SDG 15 “Ensure conservation of mountain ecosystems and
halt biodiversity loss”
• Philippines, a hotspot for forest and biodiversity loss can adapt the methodological framework that we
piloted in Latin America (LAM)

12. Leo Kris Mariano Palao
Geospatial Specialist/Scientist, Data Intelligence Hub, CIAT-Asia
International Center for Tropical Agriculture (CIAT)
2nd Floor UPLBFI Building
l.palao@cgiar.org
Let’s collaborate!

13. Thank you!