Land Use Change in Amazonia: Institutional Analysis and Modeling at Multitemporal and Spatial Scales (LUA/IAM)

Gilberto Câmara and Maria Isabel Sobral Escada II Reunião Anual de Avaliação do Programa FAPESP Sobre Mudanças ClimáticasProcess Number: 2008/58112-0Duration: 01/03/2010 a 28/02/2014

São Paulo, 18 e 19 de fevereiro de 2016

Team

Pesquisadores

Gilberto Câmara (OBT INPE/Univ Munster)

Yosio Shimabukuro (OBT INPE)

Roberto Araújo de Oliveira Santos Junior CST/INPE

Corina Freitas OBT/INPE

Luciano Dutra OBT/INPE

Sidnei Santana OBT/INPE

Myanna Lansen (CST/INPE)

Patrícia Pinho (CST/INPE)

Leila Fonseca (OBT/INPE)

Thales Korting (OBT/INPE)

Maria Isabel Sobral Escada (OBT/INPE)

Silvana Amaral IOBT/INPE)

Ana Paula Dutra Aguiar (CST/INPE)

Pedro Andrade Ribeiro Neto (CST/INPE)

Fernando Ramos (LAC/INPE)

Colaboradores

Rene Poccard Chapuis (Cirad)

Mateus Batistella - Embrapa

Eduardo Brondízio – Univ de Indiana

Andrea Siqueira – Universidade de Indiana

Dengsheng Lu – Universidade de Indiana

Tiago Carneiro (UFOPA)

Ima Vieira (MPEG)

Pos Doc, Doutorado, Mestrado

Carolina Pinho pos doc

Nathan Vogh – pós doc

Alexandre Noma – pós doc

Vagner Camilotti - doutorando

Ana Paula Dal´Asta - doutorando

Elói de La Nora – doutorando

Taise Pinheiro - doutorando

Victor Maus - doutorando

Mariane Reis - mestranda

Juliana Mota de Siqueira

Giovana Espíndola - phd

Thalita - mestrado

André Gavlak – mestrado

Sérgio costa - phd

LUA/IAM Main Goal

To study the coevolution of institutional arrangements and land change in Amazonia, to achieve a broader understanding of their

impacts to regional and global environmental changes.

Institutions: Rules and Norms system organized and incorporated by social structure. (Ostrom, 1990). (legal convention and informal constraint)

1. How the social actors deal with norms and rules?

2. How norms and rules affect land change ?

3. How to model land change based on the analysis of Institutional arrangements?

LUA´s Lines of research – 4 axis

1. Social analysis for identification and

analysis of institutional arrangements that

influence land change

2. Measurement and mapping of land change

using novel remote sensing and image

processing methods

3. Detection and analysis ofland occupation patterns and

trajectories emerging from land changes related to theinstitutional arrangements

Development of tools for detecting, mapping and modeling temporal and spatialland change patterns: GeoDMA and TerraME

4. Construction of computational models and scenarios that capture

how social interactions and Institutional arrangements act on

land change.

LUA combines different research areas and conceptual works with toolboox

and method building.

Axis 1. Analysis of institutional arrangements that influence land change

Floresta Estadual e Nacional

Reserva Extrativista, Florestal e de desenvolvimento Sustentável

Área de Proteção Ambiental, Especial e de Relevante Interesse Ecológico

Projeto de Assentamento - PA

Projeto de Assentamento Agroextrativista - PAE

Projeto de Desenvolvimento Sustentável - PDS

Projeto de Assentamento Coletivo - PAC

Terras Indígenas

UC Proteção Integral

Period Categories of territory occupation Actors

Up to 1960 Public Land Appropriation (posse) Rubber farms and extratives

population

1965 - 1985 Public or private colonization, market land - Private capital or

appropriation of public lands

INCRA

Fams, Large companies

1988 -2000 SNUC – New category of Protection area – Sustainable Use

Area

Extrative; quilombolas

2000 - 2012 Multiplication of PAE, PAF, RESEX, creation of new protection

areas and indigenous land

Extrative population, indigenous,

quilombolas population.

Mosaic of Territorial Unities

From 2000 to 2012 :

• 112 Protection Areas

• 196 Protection Areas of Sustainable Use

• 66 Indigenous Land

• 358 Agro-extractive Settlement Project – INCRA

• Regularization of lands

Santos Jr., R. A., et al “Productive forests, communities and land use change an institutional Arrangement Analysis

In Western Amazonia".

Map produced by Aguiar, A. P

Source: MMA, IBAMA, ICMBIO, INCRA, ITERPA FUNAI

Institutional Arrangements Governing Land Use in Territorial Units

2000

2010

Land Cover, Roads and Territorial Units

1985

Santos Jr, R. A.; Aguiar, A. P.Vogh.,

Axis 1, 2 and 3. Developing Hierarchical Land-Cover Classification Key and Change Classe: Draft of Cross-Axis Land Cover Legend Translation Effort for Amazon Basin (Uplands)

FAPESP Research Program in e-science - Grant 2014/08398-6

Big Earth observation data analytics for land use and land cover change information

Project duration: January 01, 2015 – 31 December 2018

Land Cover Meta-Language (LCML- FAO)

Landsat/TM – 30m

Modis – 250m

Field work

Reis et al, under development

Axis 2. Measurement and mapping of land change, using novel remote sensing and image processing methods

Combining optical and SAR data for land cover classification using polarimetric radar data (ALOS/PALSAR and RADARSAT2), fused with optical remote sensing

complex biophysical environment and the high and persistent cloud cover during the most part of the year.

Radar data is not sensitive to clouds

different fusion techniques

Radar image - color composition R(HH)-G(HV)-B(HH).

Measurement and mapping of land change, using novel remote sensing and image processing methods

Landsat TM image provides higher land cover classification accuracy than radar datasets

The accuracy is improved when textural images are combined with multispectral bands;

When optical sensor data is not available due to cloud cover, radar data are valuable for coarse land cover classification.

L-band data can provide better classification than C-band

the use of texture attributes is recommended to improve classification accuracy

Comparison of classification results for the Altamira study area: (left) Landsat TM image; (middle) PALSAR L-band data; (right) TM multispectral and PALSAR L-band HH fusion image with the wavelet-merging technique.

Lu et al. GIScience & Remote Sensing,2011

Associando padrões a processos de mudança de cobertura da terra

1. Geométrica

4. Difusa

3. Espinha-de-peixe2. Corredor

5. Manchas 6.Ilhas

1. Geométrica

4. Difusa

3. Espinha-de-peixe2. Corredor

5. Manchas 6.Ilhas

Axis 3. Deforestation patterns associated to Land Occupation(Lambim, 1994; Mertens e Lambim, 1999; Geist e Lambim, 2001)

1) Geométrica – Áreas de

fazendas com finalidade

Comercial – Expansão agrícola

2) Corredor – Colonização

Espontânea

3) Espinha-de-peixe –

Assentamento planejado

4) Difusa – agricultura tradicional,

Subsistência

5) Manchas/Fragmentos – Áreas

com Manchas residuais de

Floresta

6) Ilhas – Áreas peri-urbanas

Axis 3. Detection and description of occupation patterns and trajectories:Land change patterns and populational dynamic in the Sustainable Forest

District of Br 163 (DFS BR-163)

Typology of Land Occupation Patterns and Frontier stages based on Deforestation Data

Prodes 1997, 2000, 2003, 2007.

Land Occupation Patterns 1997, 2000, 2003, 2007

Gavlak et al (2011)

Dynamic of Potential Spatial Population Distribution using

Multivariated Model

Trajectories of Land Occupation Patterns

Secondary Vegetation Dynamic

Axis 3. Integration of land occupation patterns with land cover and population data in a cell space: The frontiers dynamic in a novel territory unit from 2000 to 2007.

The Sustainable Forest District of Br 163 road

Gavlak et al (2011), Amaral et al (2012), Population and Environment, 2012

Entorno de Santarém - A expansão da soja:

Impactos sobre a estrutura das terras

1990 1999 2010

1990 a 2010: aumento dos

padrões

associados à concentração

de terras para agricultura

e pecuária em larga escala

(geométrico, geométrico

contínuo e misto)

258330

124

Dal’Asta et al (2013)

Axis3. Detection and description of occupation patterns and trajectories: Mapping and characterizing urbanized nucleus

Dal’ Asta, A . P. et al – Remote Sensing, 2012

Field work

Spatial Unities of Occupation

Example: Riverine Communities on Lower Tapajós

Conjunto Variáveis Atributos

Comunidade Unidade de Conservação ausência [0]/ presença [1]

Margem Margem direita [0]/ margem esquerda [1]

Número de pessoas 0 [0] a 350 [0.8]/ 400 [0.81] a 1000 [1]/ >1000 pessoas[1]

Bolsa Família nada [0]/ pouco [0.3]/muito [0.6]/ maioria [0.8]/ todos [1]

Instituições ausência [0]/ presença [1]

Número de associações ausência [0]/ 5 [1]

Idade da comunidade 0 a 130 anos [0 - 0.77]/ 131 a 320 [0.78 - 1]

Saúde e educação

Ensino infantil ausência [0]/ presença [1]

Ensino fundamental 2º ciclo ausência [0]/ presença [1]

Merenda nada[0]/<10% [0.10]/ <30% [0.33]/>25 <50% [0.38]/<50% [0.47]/50% [0.5]/< 67% [0.63]/67% [0.67]/75% [0.75]/>80%

[0.79]/83% [0.83]/>67 <100% [0.87]/90% [0.92]/100%[1]

EJA ausência [0]/ presença [1]

Posto de Saúde ausência [0]/ presença [1]

Saúde e Alegria ausência [0]/ presença [1]

Infra-estrutura

Energia ausência [0]/ gerador [0.5]/ hidroelétrica [1]

Água poço e/ou rio [0]/ poço artesiano e/ou encanada [1]

Lixo descarte e/ou céu aberto [0]/ Queima e/ou enterra [0.5]/ coleta e/ou aproveitamento [1]

Telefone ausência [0]/ só celular [0.5]/ orelhão e/ou fixo [0.8]/ ambos [1]

Mercado, bar e restaurante ausência [0]/ 6 [1]

Campo de futebol ausência [0]/ presença [1]

Igrejas ausência [0]/ evangélica ou católica [0.5]/ evangélica e católica [1]

Mantimentos origem não compra [0]/outras cmm [0.25]/outras cmm e cidade; local e outras cmm; cmm, cidade e outras cmm [0.5]/cidade [0.7]/

local [1]

Uso da terra Arroz ausência [0]/ presença [1]

Mandioca ausência [0]/ presença [1]

Frutas ausência [0]/ presença [1]

Castanha ausência [0]/ presença [1]

Açaí ausência [0]/ presença [1]

Pesca ausência [0]/ presença [1]

Caça ausência [0]/ presença [1]

Gado ausência [0]/ comércio local [0.5]/ comércio para outras cmm [1]

Mineração ausência [0]/ presença [1]

Mapping and Characterizing urbanized nucleus

Field campaign:62 Tapajós49 Arapiuns55 Upland DFS br-163

Amaral et al, REBEP, 2013

Description of networks:Transport

Pinho, C. D. M, 2012 - Thesis

Connectivity

Index

)(

)(

inout

inout

GG

GGIc

Eixos 1 e 4. What are the relations between changes in land use and the evolution of institutional arrangements in Amazonia?

How exogenous forces influence agents’ decisions represented by land market, the moving frontier and the institutional arrangements?

How they operate in shaping the evolution of deforestation?

Agent-based models of deforestation (Sergio Costa’s PhD, 2012)

Migration

Small-scale

extensive

farming

Large-scale

extensive

farming

Intensive

farming

Speculation

Abandoning

Rural

activity

Initial state Final

state

Agent states in São Felix do Xingu

Deforestation:simulated x observed

2005

2000 2005

Observed deforestation patterns

Model results – Simulation 3

Special areas Deforestation

2000 2010

2010

Period Institutional Arrangement

1985-1996

1997-2004

2005-2010

Private capitalist occupation

Beef market chain organization

Deforestation control

Amazônia in 2007 x All Variables

Variables

Transportation (11)

Distance Markets(7)

Demography (3)

Tecnology (2)

Environmental (20)

Public Policy(8)

Market (8)

Agrarian Structure(6)

Statistical analysis of deforestation

(Espindola et al., 2012)

Espíndola, G. et al, Applied Geography, 2012.

How have deforestation drivers changedbetween 1997 and 2007?

ModelR2 = 0.85 1997R2 = 0.88 2007

indigenous lands were

important in preventing

deforestation in 2007

Protected areas were

important in preventing

deforestation in 1997

Source: Espindola et al. (2012)

GLOBIOM: Global Biosphere Management Model

Partial equilibrium model: Agriculture, Forestry and Bioenergy sectors

MARKETS

Population & Economic Growth & Exogenous Demand Shocks

CommodityPrices and Quantities

Land Use

Environmental effects

LAND

SPA

TIA

L R

ESO

LUTI

ON

REG

ION

Wood Crops

Forest Cropland Pasture Other

Livestock

DEMAND

SUPPLY

source: IIASAREDD-PAC project (IIASA, INPE, IPEA)

Obrigada!

isabel@dpi.inpe.br

Indicadores de Produção

Teses 13

Teses em andamento 2

Dissertações de Mestrado 7

Pos-doc 2

Publicação - periódicos 39

Capítulo de livro 4

Artigos em Congresso 37

Relatórios técnicos 5

Dezembro de 2014