The Importance of
Improving Collection and
Access to Environmental
Data in the Americas
Gilberto Câmara
Director for Earth Observation
National Institute for Space Research
With thanks to...
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Carlos Nobre, CPTEC/INPE
Antonio Nobre, INPA
Eduardo Assad, EMBRAPA
João Vianei Soares, Miguel Monteiro, INPE
Daniel Hogan, UNICAMP
Ima Vieira, Peter Toledo, Mike Hopkins, MPEG
Leandro Ferreira, Ana Albernaz, MPEG
Luiz Bevilacqua, AEB/Brazilian Academy of Sciences
José Simeão Medeiros, INPE
and the whole INPE team....
What is Environmental Data?
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Environment == “catch-all” word
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“Enviromental Data”  Earth Sciences data
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Athmosphere, oceans, biosphere
General feature
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Collected on a geographical location
Either “in situ” or by remote sensing
In many cases, in “someone else’s backyard”
LBA Flux Towers on Amazonia
Source: Carlos Nobre (INPE
Source: Carlos Nobre (INPE
Biodiversity...
CBERS Image
Challenges of Sustainable
Development
Unlike other factors of production (such as capital and
labor), natural resources are inflexible in their location.
The Amazonian Forest is where it is; the water resources
for our cities cannot be very far away from them.
The challenge posed by sustainable development is that
we can no longer consider natural resources as
indefinitely replaceable, and move people and capital to
new areas when existing resources become scarce or
exhausted: there are no new frontiers in a globalized
world.
(Daniel Hogan)
Sustainability Science Core
Questions
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How can the dynamic interactions between nature and
society be better incorporated in emerging models and
conceptualizations that integrate the earth system,
human development and sustainability?
How are long-term trends in environment and
development, including consumption and population,
reshaping nature-society interactions in ways relevant to
sustainability?
What determines vulnerability/resilience of naturesociety interactions for particular places and for
particular types of ecosystems and human livelihoods?
Source: Sustainability Science Workshop, Friibergh, SE, 2000
Sustainability Science Core
Questions
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Can scientifically meaningful ‘limits’ or ‘boundaries’ be
defined that would provide effective warning of
conditions beyond which the nature-society systems
incur a significantly increased risk of serious
degradation?
How can today’s relatively independent activities of
research planning, monitoring, assessment and decision
support be better integrated into systems for adaptative
management and societal learning?”
Source: Sustainability Science Workshop, Friibergh, SE, 2000
Public Policy Issues
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What are the acceptable limits to land cover change
activities in the tropical regions in the Americas?
What are the future scenarios of land use?
How can food production be made more efficient and
productive?
How can our biodiversity be known and the benefits
arising from its use be shared fairly?
How can we manage our water resources to sustain our
expected growth in urban population?
The Importance of Environmental
Data
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Our knowledge of earth system science is very
incomplete
Support for earth science modelling
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Helps address sustainability science questions
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Understanding of processes
Supporting “conjectures and refutations”
From scientific questions to public policy issues
Data collection brings new questions and helps formulate
new ones
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Breaking the five orders of ignorance
The Five Orders of Ignorance
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0th Order Ignorance (0OI): Lack of Ignorance
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1st Order Ignorance (1OI): Lack of Knowledge
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I do not know that I do not know something
3rd Order Ignorance (3OI): Lack of Process
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I do not know something
2nd Order Ignorance (2OI): Lack of Awareness
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I (provably) know something
I do not know a suitably effective way to find out that I don’t know that
I don’t know something
4th Order Ignorance (4OI): Meta-Ignorance
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I do not know about the Five Orders of Ignorance
The five orders of ignorance, Phillip G. Armour, CACM, 43(10), Oct 2000
Why is Environmental Data
Different?
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Cannot be re-created or synthesized in a laboratory
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Unlike data in Physical, Chemical and Biological Sciences
Requirement of access to a data collection size
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Granted by mutual consent
Implicitly conceded by international conventions
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Remote Sensing is ruled by COPUOS
Biodiversity collection is guided by Biodiversity convention
Extremely sensitive topic
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Many governments and politicians think of data collection as
“stealing our valuable resources”
Amazonia (LBA - GEOMA):
Scientific Questions that need Good Data
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What is the age of the trees in Amazonia?
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What is the extension of the Amazonian wetlands?
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What is the environmental impact of the forest fires?
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What is the CO2 balance of the rain forest?
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What are the driving factors of deforestation?
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What are the true extent of biodiversity in Amazonia?
The Challenges
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Data Collection over large regions is tough work...
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Consequences
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Can indirect data help?
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Sparse data
In many cases, limited by reachability of field campaigns
Fast degradation of infra-structure
How can improvements in Remote Sensing help us?
There is a need for much more in situ data collection
What do you do with bad or incomplete data?
LBA Sites
Operational site
Planned site
Up to 5 years of data
Up to 3 years of data
1 to 2 years of data
Dados com boa taxonomia e bons dados de distribuição.........
Flora Neotropica etc: Mimosoideae: Inga; Lauraceae:
Nectandra; Sapotaceae, Chrysobalanaceae, algumas Annonaceae,
Marantaceae: Montagma, etc, 1425 spp geo-referenciadas até
grau de longitude/latitude e mapeadas em Arcview.
Data from
Floras..................
Reserva Ducke:
“Best kinown area in Amazonia” in
1993 (ca. 1100 spp.)
By 1999, it had 2175 species,
including between 50 – 100
undescribed ones........
Também:
Saül (Guiana Francesa – Mori et al.) – 1997 & 2002.
Iquitos (Vásquez et al.) - 1997
Flora of Ecuador (Renner et al.) – em progresso
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What are we doing?
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INPE’s role
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Production of basic data
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CBERS, LANDSAT, NOAA imagery
LBA data
Integration of Remote Sensing, GIS, Meteorology, Climatology,
Earth Sciences in Environmental Models
Some Programmes we are participating
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Monitoring Forest Fires
Monitoring and Modelling Deforestation
LBA Experiment in Amazonia
Land management and zoning for Brazil
Land Management: Dealing with Old Data
Land Management: Dealing with Old Data
Land Management: RADAM x SRTM
Land Management: RADAM x LANDSAT/NASA
Fire Monitoring in Brazil
Landsat/CBERS
Reception
NOAA Reception
Imagem TM
NOAA Image
Cartographic Base
Products
Internet
CPTEC
Weather Forecast
Decision Making
“Risque” soil moisture model (Woods Hole)
integrated with INPE/CPTEC data/models
•D. Nepstad
•C. Nobre
•A Setzer
•J. Tomasella
•U. Lopes
•P. Lefebvre
CO2 FLUXES OVER PANTANAL REGION UNDER DRY AND FLOOD CONDITIONS
POSTER
Pantanal
nov/01 - may/02
300
Fc cumulative (kg C / ha)
200
100
20 cm
Start of flooding
water layer height
20 cm
10 cm
55 cm
14 cm
0
-100
-200
-300
Simple average, 1 h averag. time
-400
-500
27/11
Recursive filter, 1/2 h averag. time
17/12
6/1
26/1
15/2
7/3
27/3
16/4
6/5
Date
Plinio Alvalá1, C. von Randow2, A. O. Manzi2, A. de Souza3, L. Sá1, R. Alvalá1
26/5
Deforestation...
What Drives Tropical Deforestation?
% of the cases
 5% 10% 50%
Underlying Factors
driving proximate causes
Causative interlinkages at
proximate/underlying levels
Internal drivers
*If less than 5%of cases,
not depicted here.
source:Geist &Lambin
1973
Courtesy: INPE/OBT
1991
Courtesy: INPE/OBT
1999
Deforestation in Amazonia
PRODES (Total 1997) = 532.086 km2
PRODES (Total 2001) = 607.957 km2
Desmatamentos Ocorridos em Áreas Prioritárias à Conservação-2002
PA
AM
TO
MT
Desmat. em Área Prioritária
Desmat. em Outras Áreas
Fonte: MMA/SBF
Modelling Tropical Deforestation
•Análise de tendências
•Modelos econômicos
Coarse: 100 km x 100 km grid
Fine: 25 km x 25 km grid
Factors Affecting Deforestation
Category
Demographic
Technology
Variables
Population Density
Proportion of urban population
Proportion of migrant population (before 1991, from 1991 to 1996)
Number of tractors per number of farms
Percentage of farms with technical assistance
Agrarian strutucture Percentage of small, medium and large properties in terms of area
Percentage of small, medium and large properties in terms of number
Infra-structure
Distance to paved and non-paved roads
Distance to urban centers
Distance to ports
Economy
Distance to wood extraction poles
Distance to mining activities in operation (*)
Connection index to national markets
Percentage cover of protected areas (National Forests, Reserves,
Political
Presence of INCRA settlements
Number of families settled (*)
Environmental
Soils (classes of fertility, texture, slope)
Climatic (avarage precipitation, temperature*, relative umidity*)
Coarse resolution: candidate models
MODEL 7:
Variables
R² = .86
PORC3_AR
Description
Percentage of large farms, in terms of
area
LOG_DENS
Population density (log 10)
PRECIPIT
stb
p-level
0,27
0,00
0,38
0,00
-0,32
0,00
LOG_NR1
Avarege precipitation
Percentage of small farms, in terms of
number (log 10)
0,29
0,00
DIST_EST
Distance to roads
-0,10
0,00
LOG2_FER
Percentage of medium fertility soil (log 10)
-0,06
PORC1_UC
Percantage of Indigenous land
-0,06
0,01 MODEL 4:
Variables
0,01
R² = .83
Description
stb
p-level
CONEX_ME
Connectivity to national markets index
0,26
0,00
LOG_DENS
0,41
0,00
0,38
0,00
PORC1_AR
Population density (log 10)
Percentage of small farms, in terms of
number (log 10)
Percentage of small farms, in terms of
area
-0,37
0,00
LOG_MIG2
Percentage of migrant population from 91
to 96 (log 10)
0,12
0,00
LOG2_FER
Percentage of medium fertility soil (log 10)
-0,06
0,01
LOG_NR1
Coarse resolution: Hot-spots map
Terra do Meio
South of Amazonas State
Hot-spots map for Model 7:
(lighter cells have regression residual < -0.4)
Modelling Deforestation in Amazonia
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High coefficients of multiple determination were obtained on all
models built (R2 from 0.80 to 0.86).
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The main factors identified were:
Population density;
 Connection to national markets;
 Climatic conditions;
 Indicators related to land distribution between large and small farmers.
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The main current agricultural frontier areas, in Pará and Amazonas
States, where intense deforestation processes are taking place now
were correctly identified as hot-spots of change.
Deforestation Alert – Sensors
TERRA e AQUA
MODIS - Moderate-resolution
Imaging Spectroradiometer
36 bandas
Resolução temporal: Diária
Resolução espacial: 250 m
CBERS - China-Brazil Earth Resources
Satellite
Sensor WFI
2 bandas
260 m de resolução
Repetitividade: 5 dias
MODIS R (MIR) G (NIR) B (RED) - 08/AGOSTO/2003
MODIS R (MIR) G (NIR) B (RED) - 09/AGOSTO/2003
MODIS R (MIR) G (NIR) B (RED) - 10/AGOSTO/2003
MODIS R (MIR) G (NIR) B (RED) - Mosaico/AGOSTO/2003
PRODES Digital 2002 - MODIS MAIO 2003 (RGB)
PRODES Digital 2002 - MODIS JUNHO 2003 (RGB)
PRODES Digital 2002 - MODIS JULHO 2003 (RGB)
Environmental Modelling in Brasil
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GEOMA: “Rede Cooperativa de Modelagem Ambiental”
Cooperative Network for Environmental Modelling
 Established by Ministry of Science and Technology
 INPE/OBT, INPE/CPTEC, LNCC, INPA, IMPA, MPEG
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Long-term objectives
Develop computational-mathematical models to predict the spatial
dynamics of ecological and socio-economic systems at different
geographic scales, within the framework of sustainability
 Support policy decision making at local, regional and national levels, by
providing decision makers with qualified analytical tools.
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Environmental Modelling in Brazil
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GEOMA Network
Three Year Focus (2003-2006)
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Amazon region
Modelling
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Land Use and Land Cover Change
Population dynamics
Wetlands
Biodiversity
Hydrological systems
Regional economics
The Road Ahead: Can Technology Help?
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Advances in remote sensing are giving computer
networks the eyes and ears they need to observe their
physical surroundings.
Sensors detect physical changes in pressure,
temperature, light, sound, or chemical concentrations
and then send a signal to a computer that does
something in response.
Scientists expect that billions of these devices will
someday form rich sensory networks linked to digital
backbones that put the environment itself online.
(Rand Corporation, “The Future of Remote Sensing”)
The Road Ahead: Smart Sensors
SMART DUST
Autonomous sensing and
communication in a cubic
millimeter
Sources: Silvio Meira and Univ Berkeley, SmartDust project
The Road Ahead: Improving Models
The Carbonsink of Amazonian Forest
and climate
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Sink Strength
1 to 7 t C ha-1 yr-1
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1  0.5?
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Preliminary synthesis of the carbon cycle for Amazonian forests.
Units: t C ha-1 yr-1. GPP= gross primary productivity; Ra= autotrophic respiration;
Rh=heterotrophic respiration; VOC= volatile organic carbon compounds.
Source: Carlos Nobre, Alterra, INPA, IH, Edinburgh Un., Washington Un.
Source: LUCC
Uncertainty on basic equations
Limits for Models
Social and Economic
Systems
Quantum
Gravity
Particle
Physics
Living
Systems
Global
Change
Chemical
Reactions
Applied
Sciences
Solar System Dynamics
Complexity of the phenomenon
Meteorology
source: John Barrow
The Road Ahead...
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Producing environmental data in the Americas
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Tremendous impact of in the management of our natural
resources
Task outside of the resources and capabilities of a single country
Breaking the bottleneck
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Establishment of continental research networks
Adherence to agreed international protocols (Biodiversity
Convention, Kyoto Protocol)
The Rôle of Science and Scientists
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Science is more than a body of knowledge; it is a way of
thinking. [...]
The method of science ... is far more important than the
findings of science. (Carl Sagan)
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Scientists have to understand the sensitivities involved in
collecting, using and disseminating environmental data