1. The Importance of Improving Collection and Access to Environmental Data in the Americas
Gilberto Câmara
Director for Earth Observation
National Institute for Space Research

2. 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....

3. 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”

4. LBA Flux Towers on Amazonia
Source: Carlos Nobre (INPE)

5. Source: Carlos Nobre (INPE)
Biodiversity...

6. CBERS Image

7. 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)

8. 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

9. 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

10. 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?

11. 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

12. 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

13. 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”

14. 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?

15. 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?

16. LBA Sites Operational site Planned site Up to 5 years of data

17. 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.

18. Data from Floras.................. Reserva Ducke:
“Best kinown area in Amazonia” in 1993 (ca 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.)
Flora of Ecuador (Renner et al.) – em progresso

19. Sapotaceae “densidade das espécies”
Alto Rio Negro
Saül
Belém
Santarém
Tabatinga
Rio de Janeiro

20. “1425 espécies”
Isso é realmente a distribuição da diversidade de espécies neotropicais???
De jeito nenhum!!!!!

21. What are we doing? INPE’s role Some Programmes we are participating
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

22. Land Management: Dealing with Old Data

23. Land Management: Dealing with Old Data

24. Land Management: RADAM x SRTM

25. Land Management: RADAM x LANDSAT/NASA

26. Landsat/CBERS Reception
Fire Monitoring in Brazil
Cartographic Base
Imagem TM
Landsat/CBERS Reception
FOREST FIRE MONITORING
Products
NOAA Image
NOAA Reception
Internet
CPTEC
Weather Forecast
Decision Making

28. “Risque” soil moisture model (Woods Hole)
integrated with INPE/CPTEC data/models
D. Nepstad
C. Nobre
A Setzer
J. Tomasella
U. Lopes
P. Lefebvre

29. CO2 FLUXES OVER PANTANAL REGION UNDER DRY AND FLOOD CONDITIONS
POSTER
20 cm
10 cm
Start of flooding
water layer height
20 cm
55 cm
14 cm
Plinio Alvalá1, C. von Randow2, A. O. Manzi2, A. de Souza3, L. Sá1, R. Alvalá1

30. Deforestation...

31. 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

33.
Courtesy: INPE/OBT

34.
Courtesy: INPE/OBT

35. Deforestation in Amazonia
PRODES (Total 1997) = km2
PRODES (Total 2001) = km2

36. Desmatamentos Ocorridos em Áreas Prioritárias à Conservação-2002PA AM TO MT
Desmat. em Área Prioritária
Desmat. em Outras Áreas
Fonte: MMA/SBF

37. Modelling Tropical Deforestation
Análise de tendências
Modelos econômicos
Coarse: 100 km x 100 km grid
Fine: 25 km x 25 km grid

38. Factors Affecting Deforestation

39. Coarse resolution: candidate models

40. 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)

41. 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. 

42. 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

43. MODIS R (MIR) G (NIR) B (RED) - 08/AGOSTO/2003

44. MODIS R (MIR) G (NIR) B (RED) - 09/AGOSTO/2003

45. MODIS R (MIR) G (NIR) B (RED) - 10/AGOSTO/2003

46. MODIS R (MIR) G (NIR) B (RED) - Mosaico/AGOSTO/2003

47. WFI/CBERS - 25/03/2000 – Mato Grosso

48. WFI/CBERS – Mosaico Março 2000 – Mato Grosso

49. MODIS (agosto de 2000)

50. PRODES Digital 2002 - MODIS MAIO 2003 (RGB)

51. PRODES Digital 2002 - MODIS JUNHO 2003 (RGB)

52. PRODES Digital 2002 - MODIS JULHO 2003 (RGB)

53. 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.  

54. Environmental Modelling in Brazil
GEOMA Network
Three Year Focus ( )
Amazon region
Modelling
Land Use and Land Cover Change
Population dynamics
Wetlands
Biodiversity
Hydrological systems
Regional economics

55. 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”)

56. The Road Ahead: Smart Sensors
SMART DUST Autonomous sensing and communication in a cubic millimeter
Sources: Silvio Meira and Univ Berkeley, SmartDust project

57. The Road Ahead: Improving Models

58. The Carbonsink of Amazonian Forest
and climate
Sink Strength
1 to 7 t C ha-1 yr-1     
1  0.5?   2
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.

59. Source: LUCC

60. Limits for Models Uncertainty on basic equations
Social and Economic
Systems
Quantum
Gravity
Particle
Physics
Living
Systems
Global
Change
Chemical
Reactions
Applied
Sciences
Meteorology
Solar System Dynamics
Complexity of the phenomenon
source: John Barrow

61. 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)

62. 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