Forest Monitoring
Systems in Brazil
Thelma Krug, Dalton Valeriano
thelmakrug@dir.inpe.br
dalton@dsr.inpe.br
Seminar for Climate Change and Forests
International Conference Hall – Waseda University, Japan
February 7th– 8th , 2013

Summary of Presentation• Brazilian forest monitoring systems:• Annual wall-to-wall assessment of the gross deforestation rate
• PRODES, Brazilian Legal Amazonia• Use of satellite imagery of moderate resolution (30 meters)• Annual wall-to-wall assessment of the forest degradation• DEGRAD/DETEX, Brazilian Legal Amazonia• Use of satellite imagery of moderate resolution (30 meters)• Near real time detection of deforestation and forest degradation• DETER, Brazilian Legal Amazonia• Use of satellite imagery of coarse resolution (250 meters)• Present status

Annual Wall-to-Wall
Estimate of the Gross
Deforestation Rate inAmazonia - PRODESPRODES: Annual Rate of Gross Deforestation in Amazonia - Overview
Coverage: Wall-to wall
Frequency: Yearly

Brazilian Legal Amazon Region: 5 million km2
Original forest cover: 4 million km2
Present gross deforested area: 730.000 km2

Integration period: Aug-Jul
Proportion of secondary forest: 19% (130.000 km2)

Resolution – Landsat-class(Landsat, CBERS, DMC, SPOT)
Method: Visual interpretation
Minimum mapping area: 6.25 ha

Delivery Schedule:
1st estimate -90% of previous
year, ~100 Scenes : November
Complete map: April, next year233 Landsat Images

PRODES: Rate of Gross Deforestation in
Amazonia – a Brief history
1988-2002 – Annual assessments on a regular basis
1:250.000 Landsat Color composites
Visual interpretation
Digital area calculation

PRODES: Rate of Gross Deforestation in
Amazonia – a Brief history
Three bands color composite

INPE’s new development in the 90’s
SPRING – Geographic Information Processing System

Features applied to PRODES:SOIL•Linear Mixing Model•Image Segmentation
SHADOW•Object Oriented Classification•Raster and Vector Edition
GREEN VEGETATIONOF (RGB) TM IMAGE

PRODES: Rate of Gross Deforestation in Amazonia – aBrief history
1997-2005 – SPRING based digital Landsat image analysis
Linear Mixing Algorithm

Image Segmentation
Field-based classificationand edition

Typical deforestation

DIMENSIONALITY REDUCTION OF (RGB) TM IMAGEBY GENERATING SHADE FRACTION IMAGE, WHICH ALLOWSTHE DISCRIMINATION OF SURFACE TARGETS

Burned areas

Primary forest

Shadow imageRegrowth

Typical deforestation

Image segmentation and classification

Change detection with masking of previous
deforestation

PRODES: INPE’s Amazon Monitoring Program
A brief history
2005-Present: Visual intepretation in TerraAmazon GDBM Platform

TerraAmazon overview:
Alllows multisource andmultitemporal data
Allows multiple interpreters
Real-time GDB update
Lock-in lock-out control
TerraLib open code

PRODES: Rate of Gross Deforestation inAmazonia – a Brief history
2005-Present: Visual intepretation in TerraAmazon GDBM Platform

TerraAmazon overview:

Tbyte level GDB`•Interpretation time:(SPRING) DIP+Ed:20 hs(TerraAmazon) VI: 5-10 hslPRODES schedule:

1st Semester:April :Complement of 2011 inventory
2nd Semester
First deforestation estimate
Data Acquisition: Jun-Sep
Product delivery: November

o 53 30 38 s 10 24 45 - Peixoto de Azevedo/MT - Landsat 225/67
Set/2010

Jul/2011

2010 - Landsat 227/68
o 57 00 05 s 11 49 48 - Porto dos Gaúchos/MT
Jun/2010

Ago/2011

Annual Gross Deforestation Rates from 1988
to 2012 (km2/year)

Annual Deforestation Rate per State in Amazonia
1988 – 2012 (km2/year)

PRODES: Rate of Gross Deforestation in Amazonia– Internet Access
Full access to source images,maps and statistics

3000025000
20000

Average 1995-2004 = 19.500 km2

15000
10000
500002001

2003

2005

2007

2009

2011

2013

2015

20172019

Deforestation in Rondônia (detail) –2003

Deforestation in Rondônia (detail) –2004

Deforestation in Rondônia (detail) –2005

Report by automatic e-mail of
New deforestation detected inConservation Units andMunicipalities

MultidataapproachDMC
CCD/CBERSLANDSATLANDSATCBERSSISPRODESState boundaries

Monitoring the State of theForest: DEGRAD e DETEXNear Real Time Monitoring of
the Legal Amazonia - DETER

Deforestation hotspots

DETER 2011/INPE

Near Real Time Monitoring of
the Legal Amazonia - DETERCarbon maps for Brazilian AmazoniaLargest source of uncertainty regarding carbon emissions
from deforestation is the spatial distribution of biomass.
Differences in emission estimates can be on the order of 20%
using our baseline model parameters.
Estimates based on submodels B1 (Saatch et al., 2007), B3(MCT, 2010) and B4 (Saatchi et al., 2011a,b) are relatively
similar at the regional level, those based on B2 (Nogueira etal., 2008) are significantly higher.
This difference could be even higher if the same percentageof BGB in relation to AGB in all the submodels.


Root to shoot ration is 20% in B2, 28% in B3 and 30% in B1–B4.

The emission estimate differences would increase to 30% as
BGB contribution in B2 would be larger.

DETER-R : Assimilation ofPALSAR data in theDeforestation and Forest
Degradation Warning
SystemExamples of L-band deforestation detection
capability from JERS-1 GRFM Mosaics

Examples of L-band deforestation detection capability
from ALOS/PALSAR

Modeling the spatial and temporal
heterogeneity of deforestationdriven carbon emissions: the INPEEM framework applied to theBrazilian AmazonCarbon maps for Brazilian AmazoniaLargest source of uncertainty regarding carbon emissions
from deforestation is the spatial distribution of biomass.
Differences in emission estimates can be on the order of 20%
using our baseline model parameters.
Estimates based on submodels B1 (Saatch et al., 2007), B3(MCT, 2010) and B4 (Saatchi et al., 2011a,b) are relatively
similar at the regional level, those based on B2 (Nogueira etal., 2008) are significantly higher.
This difference could be even higher if the same percentageof BGB in relation to AGB in all the submodels.


Root to shoot ration is 20% in B2, 28% in B3 and 30% in B1–B4.

The emission estimate differences would increase to 30% as
BGB contribution in B2 would be larger.

Future – challenges for REDD+
 WithinBrazil

 Expansion of the deforestation and forest

degradation systems to include all biomes:
national system Adapt the present monitoring system to thespecificities of other biomes
 Data availability – some biomes require satellite
data of finer spatial resolution (less than 5meters)
 Implement a national forest inventory Going beyond fiscalization and law enforcement
 Definition of reference emission level for all
biomes (RL for Amazonia used only historicalFuture – challenges for REDD+
 WithinBrazil When conservation (mantainence of carbonstock) can be considered a mitigation activity
and not BAU?
 Estimating carbon stock change beyond IPCC
tier 1 method and conservativeness for all
biomes.
 How to treat natural disturbances?

Thank you