Sensing the Earth: From Global to Local Gilberto Câmara (INPE, Brazil)
source: USGS Slides from LANDSAT Whither GIScience? GIScience = branch of information science that deals 1973 1987 2000 Aral Sea with geographical space GIScience = branch of science that deals with geospatial information Bolivia 1975 1992 2000
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
Global Earth Observation System of Systems Permanent Vantage Points Capabilities Far. Space L 1/HEO/GEO TDRSS & Commercial Satellites LEO/MEO Commercial Satellites and Manned Spacecraft Near. Space Aircraft/Balloon Event Tracking and Campaigns Deployable Airborne Terrestrial Forecasts & Predictions User Community
Environmental geosensor networks Why are environmental geosensors important? LBA tower in Amazonia
The fundamental question of our time source: IGBP How is the Earth’s environment changing, and what are the consequences for human civilization?
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.
Deforestation is responsible for 18 -25 per cent of global carbon emissions (Prince Charles) How does anyone know? Source: Carlos Nobre (INPE)
“Despite solid improvements by scientists in monitoring deforestation, the uncertainties are still substantial”. (Science, 27 April 2007)
Earth as a system
Global Change Where are changes taking place? How much change is happening? Who is being impacted by the change?
Global Land Project • What are the drivers and dynamics of variability and change in terrestrial humanenvironment systems? • How is the provision of environmental goods and services affected by changes in terrestrial humanenvironment systems? • What are the characteristics and dynamics of vulnerability in terrestrial humanenvironment systems?
Impacts of global environmental change By 2020 in Africa, agriculture yields could be cut by up to 50% sources: IPCC and WMO
Earth observation satellites provide key information about global change
1975 1992 1986 Global remote sensing shows the big picture
Global data is not enough…we need to know what happens in the local scale Local data calibrates global models
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
Collapse of Amazon Rain Forest? 2100 2000 forest savanna caatinga pastures desert Is there a tipping point for Amazonia? source: Oyama and Nobre, 2003
Global Earth Observation System of Systems Permanent Vantage Points Capabilities Far. Space L 1/HEO/GEO TDRSS & Commercial Satellites LEO/MEO Commercial Satellites and Manned Spacecraft Near. Space Aircraft/Balloon Event Tracking and Campaigns Deployable Airborne Terrestrial Forecasts & Predictions User Community
Is there an opportunity for EOGIScience in the geosensors aimed at data: benefits to everyone global environmental change?
GIScience provides crucial links between nature and society Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources
source: USGS Slides from LANDSAT Aral Sea 1973 1987 2000 The geo-sensor web is an enabler for GIScience research on modeling change Bolivia 1975 1992 2000
The Greek vision of spatial data Euclid Egenhofer (x + y)2 = x 2 + 2 xy + y 2 spatial topology
The Greek vision of spatial data Aristotle categories - kathgoria Smith SPAN ontologies
source: USGS Slides from LANDSAT GIScience and Change: A Research Programme Understanding how humans use space Aral Sea 1973 1987 2000 Predicting changes resulting from human actions Modeling the interaction between society and nature Bolivia 1975 1992 2000
The Renaissance vision for space Kepler Frank
The Renaissance vision Galileo Batty
Geo-sensor webs already exist… LBA tower in Amazonia
A Potential Geo-sensor Web: The Land Surface Imaging Constellation TERRA (ASTER & MODIS) IRS LANDSAT RESOURCESAT ALOS SAC-C SPOT Source: Daniel Vidal-Madjar (France) CBERS
Mount Etna (2002 eruption)
Weather and climate source: WMO 11, 000 land stations (3000 automated) 900 radiosondes, 3000 aircraft 6000 ships, 1300 buoys 5 polar, 6 geostationary satellites
Brazil´s Data Collecting Satellite Network
A vision for environmental geosensors in Brazil Vision geosensors + microsatellites = glocal
ARGOS Data Collection System (16000 plats) 650, 000 messages processed daily
Data collection services Tracking Monitoring Positions collected over a fixed period of time Data from remote stations, fixed or mobile
ARGOS Marine Fisheries Service source: ARGOS • vessel name and ID, • positions and routes • catch reports, . Argos and GPS.
Argo bouy network
I am the Walrus
Geosensor networks Network of sensors that observe, record and disseminate geographically referenced information
Geosensor networks Challenge: send data from sensors to base station maximizing quality and minimizing energy consumption
Geosensors: new directions in IC technology Projeto Smart Dust “Spec” mote UC Berkeley MICA mote Intel mote
Potential Benefits of Geosensor networks Energy Ecosystems Health Water Resources Climate Agriculture Hazards Biodiversity
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/
Geosensor networks Bird monitoring in Maine Flood monitoring in England http: //envisense. org/floodnet. htm
Monitoring Tropical Forests La Selva Biological Station in Costa Rica – Carbon Fluxes
Disaster Monitoring Geo-sensor network installed in a volcano in Equador http: //www. eecs. harvard. edu/~mdw/proj/volcano/
Geosensors for monitoring forests: a vision source: Deborah Estrin (CENS, UCLA)
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)
Trends source: Deborah Estrin (CENS, UCLA)
Dengue monitoring in Recife (Brazil)
Recife 3 D – Morro da Conceição Slides: MNT e Animação 3 D - Produzido pelos Projetos SAUDAVEL , Defesa Civil /Recife e Depto Cartografia UFPE Resp. José Constatino e José Luis Potugal
Geo-sensors…from practice to theory A GIScience-oriented theory of geo-sensors
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
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
snap (1973) Aral Sea Slides snap (1987) snap (2000) from LANDSAT Snapshots 1987 snap : T Field snap : T (S V) 2000 The function snap produces a field with the state of the space at each time. Bolivia snap (1975) snap (1992) snap (2000)
Time series (deforestation in Amazonia) series : T V The function gives a set of values in time
History Well 30 Well 40 Well 56 Well 57 hist : S (T V) Series The function hist produces the history of a location in space
Trajectory trajectory : (T S) a trajectory is a changing location in time
Moving data mdata : (T S) V mdata : Trajectory V a point moving in space with changing values
Our aim. . state : (S x T) V ) We want the state of the world at all locations and all times
In practice. . 1. {hist 1(s 1), . . , histn(sn)} We have a set of time series for fixed locations 2. {snap 1(t 1), . . , snapn(tn)} We have a set of space-based snapshots 3. {mdata 1(s 1, t 1), . . , mdatan(tn)} We have a set of moving data
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
Cerrado (Brazilian savannah) Long dry season (may-october): supports a unique array of drought- and fire- adapted plant species and animals
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?
Data content – case 1 Well 30 Well 40 Well 56 Well 57 {hist 1(s 1), . . , histn(sn)} a set of time series for fixed locations
Handling a set of time series Time Series Modeling (PIRFICT-model) Model Parameters Interpolate Map (Kriging)
Fitting the model to the data
Data from all geosensors in a time: spatial field Map of Water Table Depths levels (meters) for Oct 1 st-15 th 2004
Accumulated data from geosensors: space-time series MAY JUNE AUGUST JULY SEPTEMBER Increase/decrease of water table depths (meters) at Jardim River watershed (May, June, July, August and September, 2004)
Case Study 2 : How do people use space in Amazonia? Soybeans Loggers Competition for Space Small-scale Farming Source: Dan Nepstad (Woods Hole) Ranchers
Case 2 : Land intensification in Rondônia (BR) Peasants were given lots with sizes of 25 ha to 100 ha in 1970 s. What happened from 1970 s to 2000 s? Escada, 2003. Prodes (INPE, 2000) TM/Landsat, 5, 4, 3 (2000)
Landscape Analysis: Land units and agents Space Partitions in Rondônia …linking human activities to the landscape
Agent Typology: A simple example Is it enough to describe Amazonian land use patterns? Tropical Deforestation Spatial Patterns: Corridor, Diffuse, Fishbone, Geometric (Lambin, 1997)
Landscape Ecology Metrics n n 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 77
Fragstats (patch metrics) (image from Fragstats manual) 78
Some patch metrics n PARA = perimeter/area ratio n SHAPE = perimeter/ (perimeter for a compact region) n FRAC = fractal dimension index n CIRCLE = circle index (0 for circular, 1 for elongated) n CONTIG = average contiguity value n GYRATE = radius of gyration 79
Land Clearing use size patt erns Actors Main land use Description Linear Variable (LIN) Small households Subsistence agriculture Irregular (IRR) Small farmers Cattle ranches Settlement parcels less than 50 subsistence ha. Irregular clearings near roads agriculture following settlement parcels. Small (<50 ha) Regular Medium- Midsized and (REG large farms ) (>50 ha) Cattle ranching Settlement parcels less than 50 ha Patterns produced concentration. irregular, linear, regular by land
Decision tree for Vale do Anari
Changes in Incra parcels configuration by (Coy, 1987; Pedlowski e Dale, 1992; Escada 2003): • Fragmentation • Transference • Land concentration
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
Vale do Anari – 1985 - 1988 REG Pereira et al, 2005 Escada, 2003
Vale do Anari – 1988 - 1991 REG Pereira et al, 2005 Escada, 2003
Vale do Anari – 1991 - 1994 Pereira et al, 2005 Escada, 2003
Vale do Anari – 1994 - 1997 REG Pereira et al, 2005 Escada, 2003
Vale do Anari – 1997 - 2000 REG Pereira et al, 2005 Escada, 2003
Vale do Anari – 1985 - 2000 REG Confirmed by field work Pereira et al, 2005 Escada, 2003
Marked land concentration Government plan for settling many colonists in the area has failed. Large farmers have bought the parcels in an illicit way
In practice. . state : (S x T) V ) the previous state of the world (or a theory about it) {hist 1(s 1), . . , 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
In practice. . state : (S x T) V ) the previous state of the world (or a theory about) {snap 1(t 1), . . , 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
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)
Models: From Global to Local snap: T (S 1 V) {snap 1(t 1), . , snapn(tn)} space-based snapshots hist : S 2 (T V) the history of a location in space
The Renaissance vision for space Newton ? ? Your picture here Principia Multiscale theory of space
The trouble with current theories of scale n n Conservation of “energy”: national demand is allocated at local level No feedbacks are possible: people are guided from the above
The search for a new theory of scale n n n Non-conservative: feedbacks are possible Linking climate change and land change Future of cities and landscape integrate to the earth system
Uncertainty on basic equations Why is it so hard to model change? Social and Economic Systems Quantum Gravity Particle Physics Living Systems Global Change Chemical Reactions Hydrological Models Solar System Dynamics Complexity of the phenomenon Meteorology source: John Barrow (after David Ruelle)
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. "What´s in An Image? " COSIT´ 01, Conference on Spatial Information Theory, Morro Bay, EUA, 2001. Lecture Notes in Computer Science, vol. 2205, pp. 474 -488.
Thank you!
Скачать презентацию Sensing the Earth From Global to Local Gilberto
c58e15752772a90835c442054d8e2113.ppt