INPE´s contribution to REDD Capacity Building: data, applications, and software Gilberto Câmara...

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 <thelma.krug@dir.inpe.br>