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INPE´s contribution to REDD Capacity Building: data, applications, and softwareGilberto CâmaraDirector General National Institute for Space Research (INPE)Brazil
REDD Capacity Building
Data: INPE´s vision for the future
A constellation of satellites and sensors will provide free earth observation data for all countries on Earth
“A few satellites can cover the entire globe, but there needs to be a system in place to ensure their images are readily available to everyone who needs them. Brazil has set an important precedent by making its Earth-observation data available, and the rest of the world should follow suit.”
CBERS as a global satellite
CBERS ground stations will cover most of the Earth’s land mass between 300N and 300S
Cuiabá
Boa Vista
Chetumal
MaspalomasAswan
Jo´burg
Nairobi(?)Accra(?)
UrumchiMiyun
Ghuangzhou
Darwin(?)
Alice Springs (?)
INPE´s Remote Sensing Satellites: 2007-2020
2016
2014
CBERS-5CBERS-4
Amazônia-1
CBERS-3
2015
Amazônia-2
CBERS-6
2017
2019
CBERS-SAR
Amazônia-3
2013
2012
2011
2010
2009
2008
2007
2018
CBERS-2B
CBERS: China Brazil Earth Resources Satellite Amazônia-1: 100% Brazilian
Optical Satellites: Forestry and Agriculture
1
10
100
1 10 100 1000Resolution (metres)
Revi
sit (
days
)
WFI CBERS-2
CCD CBERS-2/3/4
AWFI CBERS-3/4
MUX CBERS-3/4
Technology 2008
Technology 2015
Technology 2000
50
50
5AWFI
CBERS-5/6
MUXCBERS-5/6
Mapping Agriculture
Mapping Forestry
Deforestation Detection
Description Land Use
5
AWFI Amaz-1/2
LANDSAT
DMC-2
500
MODIS
N.B.: DMC-2 has no global coverage
~230 scenes Landsat/year
Taxa anual de desmatamento
PRODES: Yearly detailed estimates of clear-cut areas
Applications: Deforestation monitoring
DETER: 15-day alerts of new large deforested areas
Applications: Deforestation monitoring
166-112
116-113
116-112
TerraAmazon – open source software for large-scale land change monitoring
Spatial database (PostgreSQL with vectors and images)2004-2008 data: 3 million polygons, 300 GB images, 250 GB
vector data
Methodology
Soil Image
Vegetation Image
Shade Image
Georeferencing
Import Image
Mixture Model
Interpretation and Edition
Dissemination
Classification
Segmentation
Auditing
Mixture Model
Original Image
Methodology
Input Soil Image Output Vectors
Georeferencing
Import Image
Mixture Model
Interpretation and Edition
Dissemination
Classification
Segmentation
Auditing
Segmentation
Georeferencing
Import Image
Mixture Model
Interpretation and Edition
Dissemination
Classification
Segmentation
Auditing
Classification
Methodology
K-means
classification
Input Image
Input Image and Output Clouds
MethodologyInterpreter has to “check-in” cells to work.
Georeferencing
Import Image
Mixture Model
Interpretation and Edition
Dissemination
Classification
Segmentation
Auditing
I nterpretation and Edition
Results
Final Map Classes:
Georeferencing
Import Image
Mixture Model
Interpretation and Edition
Dissemination
Classification
Segmentation
Auditing
Dissemination
ForestDeforestarionCloudsNo ForestHydrography
INPE´s new Regional Centre for Amazonia: Local and international capacity building for
monitoring tropical forests
Belém
new facilities (under construction)
INPE will promote a workshop in Belem in 2nd half of 2009 to present TerraAmazon and discuss technology transfer to rain forest nations
Next steps
Interested? email to Thelma Krug <[email protected]>
