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Near-real time pan-tropical monitoring system for detection of changes in
natural vegetation
12th Regional Workshops on Forest Monitoring GEO GFOI
Early Warning Systems for deforestation
January 19-23, 2014
San Jose Dos Campos, Brazil
Overview
 Context
 Methods
 Applications
 Impact
 Ongoing developments
 Conclusions
Context
Deforestation
52% commercial agriculture 33% small scale agriculture 7% roads construction
6% minning 2% urban expansion
5
Free data for monitoring
natural covers changes in
Latin America and the Caribbean
•A mapping tool to detect areas of rapid habitat change
•250m resolution (high percentages of disturbance events larger than
5 ha are identified)
•Frequent natural covers change monitoring, every 16 days
•Latin American and the Caribbean coverage (currently)
•Web tools available to visualize and download habitat loss data
The Bottom Line Limits...
Terra-i IS NOT the tool to
give detailed estimates of
deforestation area and
small logging activities
Terra-i could help to
prioritize high-resolution
analyses
The methods for deforestation detection only were functional for specific ecosystems
Forest monitoring
In 2006, only one tropical country monitored deforestation: Brazil
There was not an accurate estimation of de forestation (each country use different
methodologies and statistics)
To use high-frequency imaging and moderate
spatial resolution for ...
 Monitoring the conversion of natural habitats in near real time. (Results 2
months after the date of capture)
 Have a continental coverage of all types of habitat.
 Be a support for government agencies in making decisions.
 Quantifying habitat conversion rates and make analysis of trends from
2004 to date.
 Monitor the impact on protected areas in Latin America.
Terra- goals
Paula Paz
Terra-i
Jerome Touval
TNC
Andres Perez
HEIG-VD
Mark Mulligan
KCL
Karolina Argote
Terra-i
Jhon
Tello
Andy
Jarvis
Carolina
Navarrete
Alejandro
Coca
Edward Guevara
CIAT
Terra- team
Oscar Bautista
Terra-i
Louis
Reymondin
Method
The detection step (using data from 2004 to present)
2
Research methodology overview
The methodology can be split into two main steps:
The training step (using data from 2000 to 2004)
1
Bayesian-probability based neural network (BNN) learns how the greenness of a given pixel
responds to a unit of rainfall
INPUT DATA:
Vegetation Index (MOD13Q1 MODIS / NDVI Product , 16 days, 250m)
Precipitation Data: Tropical Rainfall Measuring Mission - TRMM 3B42 (3hours, 28km)
Calibrated model is run to identify fluctuations in greenness that cannot be explained by
rainfall or by previous state of the vegetation
OUTPUT DATA:
Natural cover change data (gain or loss, annual or by 16 days period, 250m)
Terra-i System
Workflow
Tiempo
Vegetation modelling
Anomaly
Modelo
Satélite
Tiempo
Vegetation modelling
OUTPUT: 16 day predicted NDVI
Prediction
Multilayer perceptron
Bayesian Neural Network (BNN)
Model trainning and noise approximation
Scaled Conjugate Gradient (SCG)
Gaussian noise
Input automatic selection
Automatic relevance determination (ARD)
The goal of the model is to predict what is the NDVI value at the date t taking as input
the NDVI values at t-1, t-2 … t-n and the previous rainfall.
INPUTS: Past NDVI (MODIS 13Q1)
Previous rainfall (TRMM 3b42)
change
Methodology – Change detection
Debido a que terra-i genera mapas de
probabilidad de conversión, se usaron
imagenes landsat para calibrar los
resultados y así seleccionar los umbrales
de probabilidad más apropiados para
cada cluster.
2004
2009
Calibration using Landsat images
34 Satellite Scenes
Vegetation change maps every 16 days
PRODUCTS
Management of massive datasets
- every 16 days we analyze 1.15 billion pixels -
Decrease Increase Flood
Terra-i System
Bolivia
Products
1 escena
TRMM
Precipitación
(3b42 v7)
+
Comparison of Terra-i results with other local models
Terra-i results were compared with deforestation data produced by the National Institute
for Space Research Instituto Nacional de Pesquisas Espaciais (INPE) from 2004 to 2009
through monitoring systems as PRODES and DETER.
PRODES
The Project of estimation of deforestation in the Brazilian Amazon (PRODES) generated
estimations from 2003 using a digital classification system with Landsat images (30m).
DETER
DETER is a near real time deforestation detection system. It publishes fortnightly
deforestation alerts for the Brazilian Amazon using MODIS images (500m).
The comparison shows a high correlation between Terra-i and PRODES
systems.
Comparación con PRODES
% de las detecciones de PRODES
dentro de los pixeles MODIS
%dedeteccionesiguales
Comparison with PRODES
A team that optimizes resources and processes
The images used MODIS and TRMM - do not have any cost. The costs are associated with the
processing equipment and hiring specialists in handling this tool
Most processes are automated using programming languages ​​like JAVA are efficient in
handling large database.
This has allowed a multidisciplinary team of four people working at 100% resulting in a
successful generation of continuous updates to the current date
The outreach of the project is fully supported by CIAT communication team and several short
reports highlighting new patterns in our data have been made available on our website.
Policy of free data access
Expert users can download our data in a format
readable in GIS software (Raster)
Users without knowledge of spatial data analysis can
visualize and downloaded our data and charts in
different format
A TOOL TO SUPPORT RESEARCH AND DECISION MAKING
http://www.terra-i.org/
GIS EspecialistsNo GIS especialists
Uses
Octubre 5, 2012Caso Tamshicayu, Perú
Detecciones Terra-i
Landsat 8
Ucayali, Perú
San Martin, Perú
Application 1: monitoring the expansion of large areas crops
Photo: A. Coca / 2013
Photo: A. Coca / 2013
Application 1: monitoring the expansion of large areas crops
Integrando
proyectos
Basado en IPCC
Application 2: understanding changes on the field (validation)
Ecoregión del Cerrado BrasileroEcoregión del Gran Chaco
Application 3: detecting changes in other ecosystems different than tropical forests
Application 4: increase product
Application 5: integration to other policy support systems
• Terra-i can also be used within the WaterWorld and Co$ting Nature Policy Support Systems to
understand the impact of recent land cover change on hydrology and the production and
delivery of ecosystem services.
• Data: http://geodata.policysupport.org/
Water flows Erosion
Impact
http://www.terra-i.org/terra-i/data/data-terra-i_peru
Reunión Lima, Marzo 2014
Terra-i Perú (Monitoring vegetal cover of a territory)
Cooperation with independent media
Plataformas en
diferentes formatos
aumentan la
participación de la
sociedad civil basada en
el uso de datos
espaciales para discutir
los eventos de su región
Impact
Feb 2012 a Dec 2014
50 daily visits
1500 users
250 institutions
185 followers
411 fans
Website
On-going development
Integration with Global Forest Watch
http://www.globalforestwatch.org/
Expand terra- Pan-tropically
En funcionamiento y actualizado En proceso de expansión
A mapping and monitoring system for rapid assessment of land cover conversion at a medium scale
(250m).
A tool for monitoring conversion of habitat at continental, national and regional level in close to real
time.
A tool for understanding the effectiveness of protected areas and other conservation measures in
stabilizing or reducing land cover conversion.
A spatial support system for decision making in public policy and private development initiatives.
Through its linkage with WaterWorld and Co$ting Nature, a system for understanding the likely impacts
of near real-time land cover change on a wide range of ecosystem services.
X Detailed monitoring tool in local level. For this it requires second-level monitoring (with high
resolution images) and third level (field data).
X A system to monitor degradation.
Is:
Is not:
Conclusions
• Deforestation is changing very fast and that
threaths several ecosistems, for this reason is very
important to have early warning systems.
• More research is still needed in order to assess
conservation policies, actions against deforestation
and diverse land and cover changes.
Conclusions
Thank you!
Contact us:
terra.i.ciat@gmail.com
l.reymondin@cgiar.org
o.bautista@cgiar.org

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Workshop usgs brasil_2015_01

  • 1. Near-real time pan-tropical monitoring system for detection of changes in natural vegetation 12th Regional Workshops on Forest Monitoring GEO GFOI Early Warning Systems for deforestation January 19-23, 2014 San Jose Dos Campos, Brazil
  • 2. Overview  Context  Methods  Applications  Impact  Ongoing developments  Conclusions
  • 4. Deforestation 52% commercial agriculture 33% small scale agriculture 7% roads construction 6% minning 2% urban expansion
  • 5. 5 Free data for monitoring natural covers changes in Latin America and the Caribbean •A mapping tool to detect areas of rapid habitat change •250m resolution (high percentages of disturbance events larger than 5 ha are identified) •Frequent natural covers change monitoring, every 16 days •Latin American and the Caribbean coverage (currently) •Web tools available to visualize and download habitat loss data The Bottom Line Limits... Terra-i IS NOT the tool to give detailed estimates of deforestation area and small logging activities Terra-i could help to prioritize high-resolution analyses
  • 6. The methods for deforestation detection only were functional for specific ecosystems Forest monitoring In 2006, only one tropical country monitored deforestation: Brazil There was not an accurate estimation of de forestation (each country use different methodologies and statistics)
  • 7. To use high-frequency imaging and moderate spatial resolution for ...  Monitoring the conversion of natural habitats in near real time. (Results 2 months after the date of capture)  Have a continental coverage of all types of habitat.  Be a support for government agencies in making decisions.  Quantifying habitat conversion rates and make analysis of trends from 2004 to date.  Monitor the impact on protected areas in Latin America. Terra- goals
  • 8. Paula Paz Terra-i Jerome Touval TNC Andres Perez HEIG-VD Mark Mulligan KCL Karolina Argote Terra-i Jhon Tello Andy Jarvis Carolina Navarrete Alejandro Coca Edward Guevara CIAT Terra- team Oscar Bautista Terra-i Louis Reymondin
  • 10. The detection step (using data from 2004 to present) 2 Research methodology overview The methodology can be split into two main steps: The training step (using data from 2000 to 2004) 1 Bayesian-probability based neural network (BNN) learns how the greenness of a given pixel responds to a unit of rainfall INPUT DATA: Vegetation Index (MOD13Q1 MODIS / NDVI Product , 16 days, 250m) Precipitation Data: Tropical Rainfall Measuring Mission - TRMM 3B42 (3hours, 28km) Calibrated model is run to identify fluctuations in greenness that cannot be explained by rainfall or by previous state of the vegetation OUTPUT DATA: Natural cover change data (gain or loss, annual or by 16 days period, 250m) Terra-i System
  • 14. OUTPUT: 16 day predicted NDVI Prediction Multilayer perceptron Bayesian Neural Network (BNN) Model trainning and noise approximation Scaled Conjugate Gradient (SCG) Gaussian noise Input automatic selection Automatic relevance determination (ARD) The goal of the model is to predict what is the NDVI value at the date t taking as input the NDVI values at t-1, t-2 … t-n and the previous rainfall. INPUTS: Past NDVI (MODIS 13Q1) Previous rainfall (TRMM 3b42) change Methodology – Change detection
  • 15. Debido a que terra-i genera mapas de probabilidad de conversión, se usaron imagenes landsat para calibrar los resultados y así seleccionar los umbrales de probabilidad más apropiados para cada cluster. 2004 2009 Calibration using Landsat images
  • 16. 34 Satellite Scenes Vegetation change maps every 16 days PRODUCTS Management of massive datasets - every 16 days we analyze 1.15 billion pixels - Decrease Increase Flood Terra-i System Bolivia Products 1 escena TRMM Precipitación (3b42 v7) +
  • 17. Comparison of Terra-i results with other local models Terra-i results were compared with deforestation data produced by the National Institute for Space Research Instituto Nacional de Pesquisas Espaciais (INPE) from 2004 to 2009 through monitoring systems as PRODES and DETER. PRODES The Project of estimation of deforestation in the Brazilian Amazon (PRODES) generated estimations from 2003 using a digital classification system with Landsat images (30m). DETER DETER is a near real time deforestation detection system. It publishes fortnightly deforestation alerts for the Brazilian Amazon using MODIS images (500m). The comparison shows a high correlation between Terra-i and PRODES systems.
  • 18. Comparación con PRODES % de las detecciones de PRODES dentro de los pixeles MODIS %dedeteccionesiguales Comparison with PRODES
  • 19. A team that optimizes resources and processes The images used MODIS and TRMM - do not have any cost. The costs are associated with the processing equipment and hiring specialists in handling this tool Most processes are automated using programming languages ​​like JAVA are efficient in handling large database. This has allowed a multidisciplinary team of four people working at 100% resulting in a successful generation of continuous updates to the current date The outreach of the project is fully supported by CIAT communication team and several short reports highlighting new patterns in our data have been made available on our website.
  • 20. Policy of free data access Expert users can download our data in a format readable in GIS software (Raster) Users without knowledge of spatial data analysis can visualize and downloaded our data and charts in different format A TOOL TO SUPPORT RESEARCH AND DECISION MAKING http://www.terra-i.org/ GIS EspecialistsNo GIS especialists
  • 21. Uses
  • 22. Octubre 5, 2012Caso Tamshicayu, Perú Detecciones Terra-i Landsat 8 Ucayali, Perú San Martin, Perú Application 1: monitoring the expansion of large areas crops
  • 23. Photo: A. Coca / 2013 Photo: A. Coca / 2013 Application 1: monitoring the expansion of large areas crops
  • 24. Integrando proyectos Basado en IPCC Application 2: understanding changes on the field (validation)
  • 25. Ecoregión del Cerrado BrasileroEcoregión del Gran Chaco Application 3: detecting changes in other ecosystems different than tropical forests
  • 27. Application 5: integration to other policy support systems • Terra-i can also be used within the WaterWorld and Co$ting Nature Policy Support Systems to understand the impact of recent land cover change on hydrology and the production and delivery of ecosystem services. • Data: http://geodata.policysupport.org/ Water flows Erosion
  • 29. http://www.terra-i.org/terra-i/data/data-terra-i_peru Reunión Lima, Marzo 2014 Terra-i Perú (Monitoring vegetal cover of a territory)
  • 30. Cooperation with independent media Plataformas en diferentes formatos aumentan la participación de la sociedad civil basada en el uso de datos espaciales para discutir los eventos de su región
  • 32. Feb 2012 a Dec 2014 50 daily visits 1500 users 250 institutions 185 followers 411 fans Website
  • 34. Integration with Global Forest Watch http://www.globalforestwatch.org/
  • 35. Expand terra- Pan-tropically En funcionamiento y actualizado En proceso de expansión
  • 36. A mapping and monitoring system for rapid assessment of land cover conversion at a medium scale (250m). A tool for monitoring conversion of habitat at continental, national and regional level in close to real time. A tool for understanding the effectiveness of protected areas and other conservation measures in stabilizing or reducing land cover conversion. A spatial support system for decision making in public policy and private development initiatives. Through its linkage with WaterWorld and Co$ting Nature, a system for understanding the likely impacts of near real-time land cover change on a wide range of ecosystem services. X Detailed monitoring tool in local level. For this it requires second-level monitoring (with high resolution images) and third level (field data). X A system to monitor degradation. Is: Is not: Conclusions
  • 37. • Deforestation is changing very fast and that threaths several ecosistems, for this reason is very important to have early warning systems. • More research is still needed in order to assess conservation policies, actions against deforestation and diverse land and cover changes. Conclusions