“Statistics from Space”, Gates Foundation
Seattle, 5-6 November 2008
INPE´s contribution to
Statistics from Space: data,
applications, and software
Gilberto Câmara
Director General
National Institute for Space Research (INPE)
Brazil
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.”
“If Brazil can do it, US can do it too”
CBERS as a global satellite
CBERS ground stations will cover most of the Earth’s land
mass between 300N and 300S
INPE’s space technology agenda
“Global EO” – Brazil as global player in earth observation
Bilateral agreements
(China, Germany, UK)
Multilateral Agreements
(CEOS, GEO)
INPE´s Remote Sensing Satellites: 2007-2020
CBERS-2B
CBERS-3
Amazônia-1
CBERS-4
CBERS-SAR
Amazônia-2
N.B.: CBERS-2, launched 2003, is still operational
CBERS-5
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
CBERS: China Brazil Earth Resources Satellite
Amazônia-1: 100% Brazilian
CBERS-6
Amazônia-3
Optical Satellites: Forestry and Agriculture
100
Technology 2000
MUX CBERS-3/4
50
Technology 2008
Revisit (days)
Forestry
Mapping
10
CCD CBERS-2/3/4
MUX
CBERS-5/6
Land Use
Description
Technology 2015
LANDSAT
DMC-2
5
AWFI
CBERS-5/6
Deforestation
Detection
AWFI CBERS-3/4
AWFI
Amaz-1/2
Agriculture
Mapping
WFI CBERS-2
MODIS
1
1
5
10
50
Resolution (metres)
100
500
1000
Sensors for monitoring tropical areas
Amazônia-1 AWFI
40 m ground resolution
5 days global coverage
780 km swath
720 km swath
CBERS-3/4 AWFI
60 m ground resolution
5 days global coverage
120 km
CBERS-3/4 CCD
20 m ground resolution
26 days global coverage
CBERS-3/4 MUX
5 m ground resolution
52 days global coverage
(5 days with mirror)
60 km
CBERS-2B Sensor Configuration
WFI 260 m (890 km)
CCD 20 m (120 km)
PAN 2.5 m (27 km)
0.4
0.5
0.7
0.9
Built by China
1.1
1.5
Built by Brazil
1.7
2.3
2.5
mm
CBERS-2 CCD, Minas Gerais, Brazil
CBERS-2B CCD-HRC combined image in
São Felix (Pará, Brasil)
Approximate scale 1:10.000
CBERS 3 – 4 Sensor Configuration
WFI 73 m (860 km)
MSS 40 m (120 km)
CCD 20 m (120 km)
MUX 10 m (60 km)
PAN 5 m (60 km)
0.4
0.5
0.7
Built by China
0.9
1.1
1.5
Built by Brazil
1.7
2.1
2.3
µm
Amazônia-1 (cooperation with UK)
AWFI
0,45-0,52 B
Spectral Bands(mm)
Spatial resolution(m)
Ground swath(km)
Revisit (days)
0,52-0,59 G
0,63-0,69 R
0,77-0,89 NIR
40
780
5
Global land imaging every 3 days together with CBERS-3
(RAL-UK will alsoinclude a 10-meter camera)
SRTM DEM Coverage
90x90m Digital Elevation Model
(30x30m withheld by US govnt)
Data: SRTM for Africa
INPE will produce and distribute an STRMbased elevation data in 30 x 30 m for Africa
Interpolation of SRTM data
Original 90x90 m
SRTM (9x zoom)
Interpolated 30x30 m
Kriged SRTM
Shaded relief from SRTM
Applications: Deforestation monitoring
~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: Sugarcane area mapping
State
Área (ha) for 2007-2008
Crop
Reform
Total
Goiás
308.840
19.451
328.291
Minas Gerais
463.007
20.159
483.166
Mato Grosso
217.762
19.913
237.675
Mato Grosso do
Sul
212.551
14.406
226.957
Paraná
514.678
26.525
541.203
São Paulo
3.946.370
278.201
4.224.571
Total
5.663.208
378.655
6.041.863
Software: Open source GIS
Visualization (TerraView)
Modelling (TerraME)
Spatio-temporal
Database (TerraLib)
Statistics (R interface)
Data Mining(GeoDMA)
TerraAmazon – open source software
for large-scale land change monitoring
116-112
116-113
166-112
Spatial database (PostgreSQL with vectors and images)
2004-2008 data: 5 million polygons, 500 GB images
Software: R-Terralib interface
R data from geoR package.
Loaded into a TerraLib database, and visualized with TerraView.
Spatial statistics functions in R can access TerraLib database
Software: Land modelling with cellular
automata
Cell Spaces
Generalized Proximity Matrix – GPM
Hybrid Automata model
Nested scales
TerraME: Develop dynamical models in cell spaces
Land Change in Amazonia (Scenario for 2015)
% deforested
Cell space model developed using TerraME
0.0 – 0.1
0.1 – 0.2
0.2 – 0.3
0.3 – 0.4
0.4 – 0.5
0.5 – 0.6
0.6 – 0.7
0.7 – 0.8
0.8 – 0.9
0.9 – 1.0
INPE´s results have worldwide impact...
…and scientific credibility
“Today, Brazil’s monitoring
system is the envy of the world.
INPE has its own remote sensing
satellite, a joint effort with China,
that allows it to publish yearly
totals of deforested land that
scientists regard as reliable.”
TerraAmazon