The Importance of Improving Collection and Access to Environmental Data in the Americas Gilberto CâmaraDirector for Earth ObservationNational Institute for Space Research

With thanks to...

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 and the whole INPE team....

What is Environmental Data?

Environment == “catch-all” word

“Enviromental Data” Earth Sciences data Athmosphere, oceans, biosphere

General feature 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)

Biodiversity...

Source: Carlos Nobre (INPE)

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 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 nature-society interactions for particular places and for particular types of ecosystems and human livelihoods?

Source: Sustainability Science Workshop, Friibergh, SE, 2000

Sustainability Science Core Questions 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

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 Our knowledge of earth system science is very

incomplete Support for earth science modelling

Understanding of processes Supporting “conjectures and refutations”

Helps address sustainability science questions From scientific questions to public policy issues

Data collection brings new questions and helps formulate new ones Breaking the five orders of ignorance

The Five Orders of Ignorance

0th Order Ignorance (0OI): Lack of Ignorance I (provably) know something

1st Order Ignorance (1OI): Lack of Knowledge I do not know something

2nd Order Ignorance (2OI): Lack of Awareness I do not know that I do not know something

3rd Order Ignorance (3OI): Lack of Process 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 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? Cannot be re-created or synthesized in a

laboratory Unlike data in Physical, Chemical and Biological Sciences

Requirement of access to a data collection size Granted by mutual consent Implicitly conceded by international conventions

Remote Sensing is ruled by COPUOS Biodiversity collection is guided by Biodiversity convention

Extremely sensitive topic Many governments and politicians think of data collection

as “stealing our valuable resources”

Amazonia (LBA - GEOMA): Scientific Questions that need Good Data What is the age of the trees in Amazonia?

What is the extension of the Amazonian wetlands?

What is the environmental impact of the forest fires?

What is the CO2 balance of the rain forest?

What are the driving factors of deforestation?

What are the true extent of biodiversity in Amazonia?

The Challenges

Data Collection over large regions is tough work...

Consequences Sparse data In many cases, limited by reachability of field campaigns Fast degradation of infra-structure

Can indirect data help? 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?

Operational siteOperational site

Planned sitePlanned site

Up to 5 years of dataUp to 5 years of data

Up to 3 years of dataUp to 3 years of data

1 to 2 years of data1 to 2 years of data

LBA Sites

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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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Sapotaceae “densidade das

espécies”

Saül

Belém

Tabatinga

Rio de Janeiro

Santarém

Alto Rio Negro

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Isso é realmente a distribuição da

diversidade de espécies neotropicais???

“1425 espécies”

De jeito nenhum!!!!!

What are we doing?

INPE’s role Production of basic data

CBERS, LANDSAT, NOAA imagery LBA data

Integration of Remote Sensing, GIS, Meteorology, Climatology, Earth Sciences in Environmental Models

Some Programmes we are participating 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

Landsat/CBERS Reception

NOAA Reception

CPTEC

Imagem TM

NOAA Image

Weather Forecast

Cartographic Base

Internet

Decision Making

Products

Fire Monitoring in Brazil

“Risque” soil moisture model (Woods Hole) integrated with INPE/CPTEC data/models

•D. Nepstad•C. Nobre•A Setzer•J. Tomasella•U. Lopes•P. Lefebvre

Pantanalnov/01 - may/02

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27/11 17/12 6/1 26/1 15/2 7/3 27/3 16/4 6/5 26/5

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Fc

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Simple average, 1 h averag. time

Recursive filter, 1/2 h averag. time

Plinio Alvalá1, C. von Randow2, A. O. Manzi2, A. de Souza3, L. Sá1, R. Alvalá1

CO2 FLUXES OVER PANTANAL REGION UNDER DRY AND FLOOD CONDITIONSPOSTER

Start of floodingwater layer height

20 cm

20 cm 10 cm

55 cm14 cm

Deforestation...

What Drives Tropical Deforestation?

Underlying Factorsdriving proximate causes

Causative interlinkages atproximate/underlying levels

Internal drivers

*If less than 5%of cases,not depicted here.

source:Geist &Lambin

5% 10% 50%

% of the cases

1 9 7 3

1 9 9 1C

ourt

esy:

IN

PE

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1 9 9 9C

ourt

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Deforestation in Amazonia

PRODES (Total 1997) = 532.086 km2PRODES (Total 2001) = 607.957 km2

Desmatamentos Ocorridos em Áreas Prioritárias à Conservação-2002

Desmat. em Área PrioritáriaDesmat. em Outras Áreas

MT

AM

PA

TO

Fonte: MMA/SBF

Modelling Tropical Deforestation

Fine: 25 km x 25 km grid

Coarse: 100 km x 100 km grid

•Análise de tendências•Modelos econômicos

Factors Affecting Deforestation

Category VariablesDemographic Population Density

Proportion of urban populationProportion of migrant population (before 1991, from 1991 to 1996)

Technology Number of tractors per number of farmsPercentage of farms with technical assistance

Agrarian strutucture Percentage of small, medium and large properties in terms of areaPercentage of small, medium and large properties in terms of number

Infra-structure Distance to paved and non-paved roadsDistance to urban centersDistance to ports

Economy Distance to wood extraction polesDistance to mining activities in operation (*)Connection index to national markets

Political Percentage cover of protected areas (National Forests, Reserves, Presence of INCRA settlementsNumber of families settled (*)

Environmental Soils (classes of fertility, texture, slope)Climatic (avarage precipitation, temperature*, relative umidity*)

Coarse resolution: candidate models

MODEL 7: R² = .86Variables Description stb p-level

PORC3_ARPercentage of large farms, in terms of area 0,27 0,00

LOG_DENS Population density (log 10) 0,38 0,00

PRECIPIT Avarege precipitation -0,32 0,00

LOG_NR1Percentage 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 0,01

PORC1_UC Percantage of Indigenous land -0,06 0,01

MODEL 4: R² = .83Variables Description stb p-level

CONEX_ME Connectivity to national markets index 0,26 0,00

LOG_DENS Population density (log 10) 0,41 0,00

LOG_NR1Percentage of small farms, in terms of number (log 10) 0,38 0,00

PORC1_ARPercentage of small farms, in terms of area -0,37 0,00

LOG_MIG2Percentage 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

Coarse resolution: Hot-spots map

Terra do Meio, Pará State

South of Amazonas State

Hot-spots map for Model 7:(lighter cells have regression residual < -0.4)

Modelling Deforestation in Amazonia High coefficients of multiple determination were obtained

on all models built (R2 from 0.80 to 0.86).

The main factors identified were: Population density; Connection to national markets; Climatic conditions; Indicators related to land distribution between large and small

farmers.

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 Spectroradiometer36 bandas

Resolução temporal: DiáriaResolução 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

WFI/CBERS - 25/03/2000 – Mato Grosso

WFI/CBERS – Mosaico Março 2000 – Mato Grosso

MODIS (agosto de 2000)

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 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

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.  

Environmental Modelling in Brazil GEOMA Network Three Year Focus (2003-2006)

Amazon region Modelling

Land Use and Land Cover Change Population dynamics Wetlands Biodiversity Hydrological systems Regional economics

The Road Ahead: Can Technology Help? 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

Sources: Silvio Meira and Univ Berkeley, SmartDust project

SMART DUST Autonomous sensing and communication in a cubic millimeter

The Road Ahead: Improving Models

The Carbonsink of Amazonian Forest

2

Sink Strength1 to 7 t C ha-1 yr-1

and climate

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.

1 0.5?

Source: LUCC

Limits for Models

source: John BarrowComplexity of the phenomenon

Un

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ain

ty o

n b

asic

eq

uat

ion

s

Solar System DynamicsMeteorology

ChemicalReactions

AppliedSciences

ParticlePhysics

Quantum Gravity

Living Systems

GlobalChange

Social and EconomicSystems

The Road Ahead...

Producing environmental data in the Americas Tremendous impact of in the management of our natural

resources Task outside of the resources and capabilities of a single

country

Breaking the bottleneck Establishment of continental research networks Adherence to agreed international protocols

(Biodiversity Convention, Kyoto Protocol)

The Rôle of Science and Scientists 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)

Scientists have to understand the sensitivities involved in collecting, using and disseminating environmental data