Presentation on theme: "Understanding Land Change in Amazonia: A Multidisciplinary Research Challenge Gilberto Câmara Director National Institute for Space Research Brazil IGERT."— Presentation transcript:
Understanding Land Change in Amazonia: A Multidisciplinary Research Challenge Gilberto Câmara Director National Institute for Space Research Brazil IGERT Colloquim Series, Department of Geography, SUNY Bufallo, February 2007
INPE - brief description National Institute for Space Research main civilian organization for space activities in Brazil staff of 1,800 ( 800 Ms.C. and Ph.D.) Areas: Space Science, Earth Observation, Meteorology and Space Engineering
Environmental activities at INPE Numerical Weather Prediction Centre medium-range forecast and climate studies LANDSAT/SPOT Receiving and Processing Station in operation since 1974 China-Brazil Earth Resources Satellite 5 bands (3 visible, 1 IR) at 20 m resol. Research Activities in Remote Sensing 300 MsC and PhD graduates ONU-funded Center for Africa and S. America
The Future of Brazilian Amazon Why is this an multidisciplinary research challenge? Amazonia is a key environmental resource Many different concerns Environment and biodiversity conservation Economic development Native population
Source: Carlos Nobre (INPE) Can we avoid that this….
Fire... Source: Carlos Nobre (INPE) ….becomes this?
Amazonia at a glance... The Natural System Almost 6 million km2 of contiguous tropical forests Perhaps 1/3 of the planet's biodiversity Abundant rainfall (2.2 m annually) 18% of freshwater input into the global oceans (220,000 m 3 /s) Over 100 G ton C stored in vegetation and soil A multitude of ecosystems, biological and ethnic diversity Source: Carlos Nobre (INPE)
We might know the past…. Estimativa do Desmatamento da Amazônia (INPE)
What’s coming next?
Deforestation... Source: Carlos Nobre (INPE)
Environmental Modelling in Brasil GEOMA: “Rede Cooperativa de Modelagem Ambiental” Cooperative Network for Environmental Modelling Established by Ministry of Science and Technology Long-term objectives Develop models to predict the spatial dynamics of ecological and socio-economic systems at different geographic scales, Support policy decision making at local, regional and national levels, by providing decision makers with qualified analytical tools.
Modelling Complex Problems Application of multidisciplinary knowledge to produce a model. If (... ? ) then... Desforestation?
What is Computational Modelling? Design and implementation of computational enviroments for modelling Requires a formal and stable description Implementation allow experimentation Rôle of computer representation Bring together expertise in different field Make the different conceptions explicit Make sure these conceptions are represented in the information system
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?
Modelling Land Change in Amazonia How much deforestation is caused by: Soybeans? Cattle ranching? Small-scale setllers? Wood loggers? Land speculators? A mixture of the above?
Challenge: How do people use space? Loggers Competition for Space Soybeans Small-scale Farming Ranchers Source: Dan Nepstad (Woods Hole)
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 5% 10% 50% % of the cases What Drives Tropical Deforestation?
Different agents, different motivations Intensive agriculture (soybeans) export-based responsive to commodity prices, productivity and transportation logistics Extensive cattle-ranching local + export responsive to land prices, sanitary controls and commodity prices
Large-Scale Agriculture Agricultural Areas (ha) /1996% Legal Amazonia5,375,16532,932, Brazil33,038,02799,485, Source: IBGE - Agrarian Census photo source: Edson Sano (EMBRAPA)
Cattle in Amazonia and Brazil Unidade % Amazônia Legal29,915,79951,689,06172,78% Brasil154,229,303176,388,72614,36% photo source: Edson Sano (EMBRAPA)
Different agents, different motivations Small-scale settlers Associated to social movements (MST, Church) Responsive to capital availability, land ownership, and land productivity Can small-scale economy be sustainable? Wood loggers Primarily local market Responsive to prime wood availability, official permits, transportation logistics Land speculators Appropriation of public lands Responsive to land registry controls, law enforcement
Altamira (Pará) – LANDSAT Image – 22 August 2003
Altamira (Pará) – MODIS Image – 07 May 2004
Imagem Modis de , com excesso de nuvens Altamira (Pará) – MODIS Image – 21 May 2004
Altamira (Pará) – MODIS Image – 07 June 2004
6.000 hectares deforested in one month! Altamira (Pará) – MODIS Image – 22 June 2004
Altamira (Pará) – LANDSAT Image – 07 July 2004
Modelling Land Change in Amazonia Territory (Geography) Money (Economy) Culture (Antropology) Modelling (GIScience)
New Frontiers Deforestation Forest Non-forest Clouds/no data INPE 2003/2004: Dynamic areas (current and future) Intense Pressure Future expansion
Amazonian new frontier hypothesis (Becker) “The actual frontiers are different from the 60’s and the 70’s In the past it was induced by Brazilian government to expand regional economy and population, aiming to integrate Amazônia with the whole country. Today, induced mostly by private economic interests and concentrated on focus areas in different regions.
Integrated Land Use and Land Cover Change Modeling in Pará
Land use and Land Cover Dynamics in São Félix do Xingu-Iriri (PA)
Iriri River S. F Xingu Xingu River Novo Progresso
Reservas Indígenas Rio Xingu Rio Iriri Transamazônica Rio Iriri Escada et al, 2005 – Estudos Avançados, Nº 54 Annual rate Accumulated Deforestation
Araújo (2004) Escada et al (2005) Land Appropriation Model Violent Expropriation Primary occupation Land permits Illegal registration Large farms Small- medium farms Legal moneyIllegal money
Source: DePará, 2005 Cattle ranching and deforestation Museu Paraense Emílio Goeldi e Embrapa Oriental Accumulated Deforestation Escada et al, 2005 – Estudos Avançados, Nº 54 Amount of cattle head
Cattle Ranching Model F F+R P PD P+R RP Forest Forest + Relief Pasture Degraded Pasture Pasture + Relief Recovered Pasture
P, M Tibornea. F. Cheiro Área em disputa (CPT, 2004) G Branquinho Cutia L. Jaba Toca do Sapo L. Caraíba Estrada Canopus Estrada dos fazendeiros P Primavera 10 km Source: CPT(2004), Taravello, R. (2004), Isa (2001), Geoma(2004), Escada et al (2005) Pequenos e Médios Grandes G P P G G P G G G G G P G T T T T Ribeirinhos P - Small G, M - Large, Medium R - Riverside Agents in Terra do Meio
Rain season flux Dry season flux Population Flux: seasonality
ESEC Terra do Meio Parque Nacional da Serra do Pardo - 5% df Canopus Fazendeiros RESEX Riozinho do Anfrísio Flona de Altamira Analysis of public policy: Conservation units in Pará Prodes 2004 (INPE, 2005) 050 km Escada et al, 2005
Sample of results Test 2: Without demand or regression regionalization; Test 8: With demand and regression regionalization (one model for fine scale partition – Arco, Central and Occidental); Test 13: With demand and regression regionalization (Arco regression model used at Central partition).
Statistics: Humans as clouds Statistical analysis of deforestation
Land Change Model ( ) 0% ->100% Federative States Roads Projected hot spots of deforestation : Percentage of change in forest cover from 1997 to 2015: Regionalizing the demand improves pressure on Central area, but Central area regressions emphasizes proximity to ports and rivers, due to historical process in the area, and not connectivity to the rest of the country.
Impact of the proposed Manaus-Porto Velho road Rede Temática GEOMA Setembro, 2006
Área de estudo – ALAP BR 319 e entorno ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais Portos new road
BASELINE SCENARIO – Hot spots of change (1997 a 2020) ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais 0.0 – – – – – – – – – – 1.0 % mudança 1997 a 2020:
GOVERNANCE SCENARIO – Differences from baseline scenario ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais Less: More: Differences: Protection areas Sustainable areas
GIScience and change Modelling change is essential in our world We need a vision for extending GIScience to have a research agenda for modeling change
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?
Limits for Models source: John Barrow (after David Ruelle) Complexity of the phenomenon Uncertainty on basic equations Solar System Dynamics Meteorology Chemical Reactions Hydrological Models Particle Physics Quantum Gravity Living Systems Global Change Social and Economic Systems