Sensing the Earth: From Global to Local

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Sensing the Earth From Global to Local
Gilberto Câmara (INPE, Brazil)
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Slides from LANDSAT
source USGS
Whither GIScience? GIScience branch of
information science that deals with geographical
space GIScience branch of science that deals
with geospatial information
Aral Sea
1973
1987
2000
Bolivia
1975
1992
2000
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My thesis today....

1. Change is a key issue in our world
2. Geo-sensor webs provide data about change
3. Geo-sensor webs already exist and new technology
   will improve them
4. Geo-sensor webs are enablers for a GIScience of
   change
5. A GIScience of change is a growing research agenda

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Global Earth Observation System of Systems
Vantage Points
Capabilities
L1/HEO/GEO TDRSS Commercial Satellites
Far-Space
Permanent
LEO/MEO Commercial Satellites and Manned
Spacecraft
Near-Space
Airborne
Aircraft/Balloon Event Tracking and Campaigns
Deployable
Terrestrial
User Community
Forecasts Predictions
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Environmental geosensor networks
Why are environmental geosensors important?
LBA tower in Amazonia
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The fundamental question of our time
How is the Earths environment changing, and what
are the consequences for human civilization?
source IGBP
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October 21 2007
Charles launches campaign to save ravaged
rainforests
Prince Charles will this week join the battle
against climate change by launching an
organisation which calls for a new green
economics that recognises the world's rainforests
are worth more alive than dead.
Deforestation is responsible for 18-25 per cent
of global carbon emissions, an output second only
to energy production.
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Deforestation is responsible for 18-25 per cent
of global carbon emissions (Prince Charles)
How does anyone know?
Source Carlos Nobre (INPE)
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Despite solid improvements by scientists in
monitoring deforestation, the uncertainties are
still substantial. (Science, 27 April 2007)
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Earth as a system
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Global Change
Where are changes taking place? How much change
is happening? Who is being impacted by the
change?
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Global Land Project

• What are the drivers and dynamics of variability
  and change in terrestrial human-environment
  systems?
• How is the provision of environmental goods and
  services affected by changes in terrestrial
  human-environment systems?
• What are the characteristics and dynamics of
  vulnerability in terrestrial human-environment
  systems?

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Impacts of global environmental change By 2020 in
Africa, agriculture yields could be cut by up to
50
sources IPCC and WMO
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Earth observation satellites provide key
information about global change
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1986
1975
Global remote sensing shows the big picture
1992
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Global data is not enoughwe need to know what
happens in the local scale
Local data calibrates global models
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Aerosol Concentrations in Amazonia Changes from
very low values of 5-12 µg/m³ to very high 500
µg/m³ In areas affected by biomass burning
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Collapse of Amazon Rain Forest?
2100
2000
savanna
forest
caatinga
pastures
desert
Is there a tipping point for Amazonia?
source Oyama and Nobre, 2003
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Global Earth Observation System of Systems
Vantage Points
Capabilities
L1/HEO/GEO TDRSS Commercial Satellites
Far-Space
Permanent
LEO/MEO Commercial Satellites and Manned
Spacecraft
Near-Space
Airborne
Aircraft/Balloon Event Tracking and Campaigns
Deployable
Terrestrial
User Community
Forecasts Predictions
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EO data benefits to everyone
Is there an opportunity for GIScience in the
geosensors aimed at global environmental change?
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GIScience provides crucial links between nature
and society
Nature Physical equations Describe processes
Society Decisions on how to Use Earths
resources
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Slides from LANDSAT
source USGS
Aral Sea
1973
1987
2000
The geo-sensor web is an enabler for GIScience
research on modeling change
Bolivia
1975
1992
2000
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The Greek vision of spatial data
(x  y)2  x2  2xy  y2
Euclid
Egenhofer
spatial topology
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The Greek vision of spatial data
Aristotle
categories - kathgoria
Smith
SPAN ontologies
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Slides from LANDSAT
source USGS
GIScience and Change A Research
Programme Understanding how humans use
space Predicting changes resulting from human
actions Modeling the interaction between
society and nature
Aral Sea
1973
1987
2000
Bolivia
1975
1992
2000
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The Renaissance vision for space
Kepler
Frank
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The Renaissance vision
Galileo
Batty
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Geo-sensor webs already exist
LBA tower in Amazonia
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A Potential Geo-sensor Web The Land Surface
Imaging Constellation
TERRA (ASTER MODIS)
IRS
LANDSAT
RESOURCESAT
ALOS
SAC-C
SPOT
CBERS
Source Daniel Vidal-Madjar (France)
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Mount Etna (2002 eruption)
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Weather and climate
11,000 land stations (3000 automated) 900
radiosondes, 3000 aircraft 6000 ships, 1300
buoys 5 polar, 6 geostationary satellites
source WMO
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Brazils Data Collecting Satellite Network
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A vision for environmental geosensors in Brazil
Vision geosensors microsatellites glocal
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ARGOS Data Collection System (16000 plats)
650,000 messages processed daily
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Data collection services
Tracking Positions collected over a fixed period
of time
Monitoring Data from remote stations, fixed or
mobile
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ARGOS Marine Fisheries Service
source ARGOS
vessel name and ID, positions and routes
catch reports,.
Argos and GPS.
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Argo bouy network
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I am the Walrus
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Geosensor networks
Network of sensors that observe, record and
disseminate geographically referenced information
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Geosensor networks
Challenge send data from sensors to base station
maximizing quality and minimizing energy
consumption
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Geosensors new directions in IC technology
Projeto Smart Dust
Spec mote UC Berkeley
MICA mote
Intel mote
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Potential Benefits of Geosensor networks
Energy
Ecosystems
Health
Water Resources
Agriculture
Climate
Hazards
Biodiversity
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Environmental Monitoring

• Redwood trees
• (Sonoma County, CA, USA)
• Temperature, humidity and light sensors
  measure the micro-climate of a redwood tree

www.eecs.berkeley.edu/get/sonoma/
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Geosensor networks

• Flood monitoring in England

Bird monitoring in Maine
http//envisense.org/floodnet/floodnet.htm
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Monitoring Tropical Forests
La Selva Biological Station in Costa Rica
Carbon Fluxes
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Disaster Monitoring

• Geo-sensor network installed in a volcano in
  Equador

http//www.eecs.harvard.edu/mdw/proj/volcano/
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Geosensors for monitoring forests a vision
source Deborah Estrin (CENS, UCLA)
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In-network and multi-scale processing
algorithms Scalability for densely deployed
sensors Low-latency for interactivity,
triggering, adaptation Integrity for challenging
system deployments
source Deborah Estrin (CENS, UCLA)
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Trends
source Deborah Estrin (CENS, UCLA)
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Dengue monitoring in Recife (Brazil)
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Recife 3D Morro da Conceição
Slides MNT e Animação 3D - Produzido pelos
Projetos SAUDAVEL , Defesa Civil /Recife e Depto
Cartografia UFPE Resp. José Constatino e José
Luis Potugal
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Geo-sensorsfrom practice to theory
A GIScience-oriented theory of geo-sensors
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A sensor (data-centric view)
Sensor measurements measure (S x T) ? V S is
the set of location T is the set of times V is
the set of values
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What is a geo-sensor?
What is a geo-sensor?
Field (static) field S ? V The function field
gives the value of every location of a space
measure (s,t) v s ? S - set of locations in
space t ? T - is the set of times. v ? V - set
of values
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Slides from LANDSAT
snap (1973)
snap (1987)
snap (2000)
Aral Sea
Snapshots snap T ? Field snap T ? (S ? V)
The function snap produces a field with the
state of the space at each time.
1987
2000
Bolivia
snap (1975)
snap (1992)
snap (2000)
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Time series (deforestation in Amazonia)
series T ? V The function gives a set of values
in time
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History
Well 30 Well 40 Well 56 Well 57
hist S ? (T?V) hist S ? Series The function
hist produces the history of a location in space
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Trajectory
trajectory (T?S) a trajectory is a changing
location in time
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Moving data
mdata (T?S) ? V mdata Trajectory ? V a point
moving in space with changing values
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Our aim....
state (S x T) ?V ) We want the state of the
world at all locations and all times
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In practice....
1. hist1(s1),...., histn(sn) We have a set of
time series for fixed locations
2. snap1(t1),...., snapn(tn) We have a set of
space-based snapshots
3. mdata1(s1, t1),...., mdatan(tn) We have a
set of moving data
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Case 1 - Water monitoring in Brazilian Cerrado

• Wells observation
• 50 points (space and time)
• 50 semimonthly time series
• 11/10/03 06/03/2007

Rodrigo Manzione, Gilberto Câmara, Martin Knotters
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Cerrado (Brazilian savannah)

• Long dry season (may-october) supports a unique
  array of drought- and fire- adapted plant species
  and animals

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The Scientific Problem

• What is the impact of agriculture in water
  content and water table depth?
• What are the consequences and risk assessment for
  water management?

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Data content case 1
Well 30 Well 40 Well 56 Well 57
hist1(s1),...., histn(sn) a set of time
series for fixed locations
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Handling a set of time series
Time Series Modeling (PIRFICT-model)
Model Parameters
Interpolate Map (Kriging)
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Fitting the model to the data
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Data from all geosensors in a time spatial field
Map of Water Table Depths levels (meters) for Oct
1st-15th 2004
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Accumulated data from geosensors space-time
series
MAY
JUNE
JULY
AUGUST
SEPTEMBER
Increase/decrease of water table depths (meters)
at Jardim River watershed (May, June, July,
August and September, 2004)
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Case Study 2 How do people use space in
Amazonia?
Loggers
Competition for Space
Source Dan Nepstad (Woods Hole)
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Case 2 Land intensification in Rondônia (BR)

Peasants were given lots with sizes of 25 ha to
100 ha in 1970s. What happened from 1970s to
2000s?
TM/Landsat, 5, 4, 3 (2000)
Prodes (INPE, 2000)
Escada, 2003.
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Landscape Analysis Land units and agents
Space Partitions in Rondônia
linking human activities to the landscape
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Agent Typology A simple example
Tropical Deforestation Spatial Patterns
Corridor, Diffuse, Fishbone, Geometric (Lambin,
1997)
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Landscape Ecology Metrics

• Patterns and differences are immediately
  recognized by the eye brain
• Landscape Ecology Metrics allow these patterns in
  space to be described quantitatively

Source Phil Hurvitz
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Fragstats (patch metrics)
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Some patch metrics

• PARA perimeter/area ratio
• SHAPE perimeter/ (perimeter for a compact
  region)
• FRAC fractal dimension index
• CIRCLE circle index (0 for circular, 1 for
  elongated)
• CONTIG average contiguity value
• GYRATE radius of gyration

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Land use patterns Clearing size Actors Main land use Description
Linear (LIN) Variable Small households Subsistence agriculture Settlement parcels less than 50 ha
Irregular (IRR) Small (lt50 ha) Small farmers Cattle ranches subsistence agriculture Settlement parcels less than 50 ha. Irregular clearings near roads following settlement parcels.
Regular (REG) Medium- large (gt50 ha) Midsized and large farms Cattle ranching Patterns produced by land concentration.
irregular, linear, regular
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Decision tree for Vale do Anari
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• Changes in Incra parcels configuration by (Coy,
  1987 Pedlowski e Dale, 1992 Escada 2003)
• Fragmentation
• Transference
• Land concentration

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Vale do Anari 1982 -1985
REG
Patterns/Typology IRR Irregular Colonist
parcels LIN Linear roadside parcels REG
Regular agregation parcels
Pereira et al, 2005 Escada, 2003
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Vale do Anari 1985 - 1988
REG
Pereira et al, 2005 Escada, 2003
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Vale do Anari 1988 - 1991
REG
Pereira et al, 2005 Escada, 2003
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Vale do Anari 1991 - 1994
Pereira et al, 2005 Escada, 2003
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Vale do Anari 1994 - 1997
REG
Pereira et al, 2005 Escada, 2003
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Vale do Anari 1997 - 2000
REG
Pereira et al, 2005 Escada, 2003
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Vale do Anari 1985 - 2000
REG
REG
Pereira et al, 2005 Escada, 2003
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Marked land concentration Government plan for
settling many colonists in the area has failed.
Large farmers have bought the parcels in an
illicit way
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In practice....
state (S x T) ?V ) the previous state of the
world (or a theory about it)
hist1(s1),...., histn(sn) a set of time
series for fixed locations
theory_time (T ?V ) a theory about the time
evolution
state (S x T) ?V ) (NEW) a new guess about
the state of the world
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In practice....
state (S x T) ?V ) the previous state of the
world (or a theory about)
snap1(t1),...., snapn(tn) a set of
space-based snapshots
theory_space (S ?V ) a theory about the
process that describe space
state (S x T) ?V ) (NEW) a new guess about
the state of the world
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Models From Global to Local
Athmosphere, ocean, chemistry climate model
(resolution 200 x 200 km) Atmosphere only
climate model (resolution 50 x 50 km) Regional
climate model Resolution e.g 10 x 10
km Hydrology, Vegetation Soil Topography (e.g, 1
x 1 km) Regional land use change Socio-economic
changes Adaptative responses (e.g., 10 x 10 m)
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Models From Global to Local
snap T ? (S1 ? V) snap1(t1),., snapn(tn)
space-based snapshots
hist S2 ? (T?V) the history of a location in
space
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The Renaissance vision for space
Principia
Newton
Multiscale theory of space
Your picture here
????
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The trouble with current theories of scale

• Conservation of energy national demand is
  allocated at local level
• No feedbacks are possible people are guided from
  the above

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The search for a new theory of scale

• Non-conservative feedbacks are possible
• Linking climate change and land change
• Future of cities and landscape integrate to the
  earth system

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Why is it so hard to model change?
Uncertainty on basic equations
Social and Economic Systems
Quantum Gravity
Particle Physics
Living Systems
Global Change
Hydrological Models
Chemical Reactions
Meteorology
Solar System Dynamics
Complexity of the phenomenon
source John Barrow (after David Ruelle)
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Some references
Frank, A.U., One Step up the Abstraction Ladder
Combining Algebras - From Functional Pieces to a
Whole. COSIT'99, 1999. Galton, A., Fields and
Objects in Space, Time, and Space-time . Spatial
Cognition and Computation, 4(1), 2004. Grenon,
P. and Smith,B. SNAP and SPAN Towards Dynamic
Spatial Ontology.Spatial Cognition
Computation, Vol. 4, No. 1 pages 69-104. M
Goodchild, M Yuan, T Cova. Towards a general
theory of geographic representation in GIS, IJGIS
2007 Marcelino P.S. Silva, G Câmara, M Escada,
Ricardo Cartaxo M. Souza, Remote Sensing Image
Mining Detecting Agents of Land Use Change in
Tropical Forest Areas. International Journal of
Remote Sensing,in press, 2008. Gilberto Câmara,
M.Egenhofer, F.Fonseca, A.Monteiro. "Whats in An
Image?" COSIT01, Conference on Spatial
Information Theory, Morro Bay, EUA, 2001. Lecture
Notes in Computer Science, vol. 2205, pp.
474-488.
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Thank you!