de presentation to iisd on may 7, 2008. title: amazon resilience and business evolution

DEDesign and Environment

Environmental Science

‣ Ecology‣ Systems Analysis‣ Remote Sensing

Design

‣ Multimedia and Film‣ Expositions / Spatial‣ Systems and Processes

Presentation to Natural Resources Analysis Group - International Institute for Sustainable Development.Winnipeg, Manitoba. May 7, 2008 Presented by: David N. Oswald

www.design-environment.com

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Presentation to Natural Resources Analysis Group - International Institute for Sustainable Development.Winnipeg, Manitoba. May 7, 2008 Presented by: David N. Oswald © 2008 DE Design and Environment Inc.

Design and Environment

1. Estimating Resilience of Amazonian Ecosystems Using Remote Sensing

II. Design, Environment and Business Evolution

DEEnvironmental Science ‣ Ecology

‣ Systems Analysis‣ Remote Sensing



Estimating Resilience of Amazonian Ecosystems Using

Remote Sensing

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Estimating Resilience of Amazonian Ecosystems Using Remote Sensing

CONTEXT

Tropical rainforests in the Amazon affected by timber harvesting, pasture conversion, and fire disturbance

Need to assess spatial patterns and ecological processes in response to disturbances

Large amount of data available from LBA (Large-Scale Biosphere Atmosphere Project in Amazonia)

Synthesis, integration, and analysis of data is required

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Time Magazine, Thursday March 27, 2008

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PROBLEM

Can disturbances such as deforestation, fire, and climate change in the Amazon basin be significant enough to cause these ecosystems to reorganize into new functional forms?

In the state of Mato Grosso, what is the resilience of rain forests to disturbances?

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Roberts et al, 2003

THE REGION

Areal extent: 5.8 million km2

Rich in biodiversity

Humid tropical rainforest that is closelycoupled with the atmosphere

Estimated that 50% of incoming precipitationis recycled

Land-use change challenges

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SOCIAL AND ECONOMIC FORCES

Deforestation is a dominant force affecting ecosystems

The “Arc of deforestation” in Mato Grosso has intense activity

Model projection of up to 40% forest loss by 2050 due to current agricultural expansion

Soares-Filho et al, 2006

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THEORETICAL CONCEPTS

Ecological Resilience

Atmosphere / Biosphere Coupling

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ECOLOGICAL RESILIENCE

Ability of these systems to absorb changes of state variables, driving variables, and parameters (Holling, 1973)

May be dictated by certain conditions (amount biomass, temperature, precipitation,nutrient availability) (Scheffer et al, 2001)

Landscape pattern may be a representation of states in ecosystems (Van de Koppel and Rietkerk, 2004)

Beisner et al, 2003

Scheffer et al, 2001

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Rainforest

Savannah

LOW Ppt / ET

Land-use / deforestation

Fire

Fueling of fire

Climate Drying

Land-use / deforestation

Fueling of fire

HIG H Ppt / ET

Fire

Climate Drying

Systems Diagram - biosphere - atmosphere coupling and alternative stable states

STATE MODEL

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Amazon Forest Ecosystems

Large Scale Pattern Large Scale Process

Small Scale Pattern (fragmented forest)

Small Scale Process (fire)

Road Building Large-scale disturbance (Fast variable)

Ppt / ET

Deforestation (Fast variable)

Burning to clear land (Fast variable)

Climate Change Large-scale disturbance (Slow variable)

Systems Diagram - forces affecting Amazonian ecosystems, feedback mechanisms, andcross-scale connections

SYSTEM MODEL

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Large scale agriculturalProduction causing significant ecological fragmentation(shown by terra preta)

Small-scale disturbanceFrom sugar cane and Cacao plantations

Increased ecological Impacts due to road formation

Start of agricultural Development from Southern Brazil

1491 1870 1950

Increased ecological Impacts due expansion in crops such as soy

Contact by Columbus Start of Rubber Boom Initiation of ‘militaristic’Development policy

Droughts in 1988/89And 2005. PressureOn ecosystems from less plant available water

1967 1995 2000

Completion of BR167 Transamazonia Highway

Implementation of Policies to supportethanol and soyproduction

Period of study2001-2006

Pre-industrial era

Industrial and Modern Era

TIME SCALE - DISTURBANCES

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Resilience View

Large-scale top-down Small-scale bottom-up Reinforcing parameters

Precipitation and Atmosphere-biosphere coupling

Population/Community integrity

Potential disturbance factors

Climate change Large scale development planning

Deforestation Fire

Spatial Scale

1 000’s – 100 000’s km2 1 – 100 km2

Temporal Scale

Decades Months to multiple years

Comparison of Resilience Views

RESILIENCE VIEWS

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Rio Madeiras 7:10 AM July 8,2006 Rio Madeiras 1:00 PM July 8,2006 Rio Madeiras 6:00 PM July 8,2006

ATMOSPHERE - BIOSPHERE COUPLING

Evaporated water recycled in boundary layer and forms precipitation

Pecipitation feeds back into rainforest

Forms a negative feedback system

In the Amazon, 50% of precipitation comes from recycled water (Salati and Vose, 1984)

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MODIS Enhanced Vegetation Index

1km Spatial resolution

16 and 30 day composites

High spectral and temporal resolution

Allows for analysis of seasonal variation in vegetation (spatial and temporal)

METHODS - VEGETATION REMOTE SENSING

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METHODS - VEGETATION REMOTE SENSING

Transect analysis - N/S and E/W

100 km x 100 km samples (sites)

General system characterization

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METHODS - ECOSYSTEM CLASSIFICATION

Average dry season values calculated from 2001 to 2006 (May to August each year)

Frequency distribution of EVI values bimodal

Field studies show similar EVI values for savanna vs. rainforest (Ratana et al, 2003)

Classification values established based on these results

Frequency of EVI observations in the Dry Season - 2001 to 2006

0

50000

100000

150000

200000

250000

300000

350000

400000

0 10 20 30 40 50 60 70 80 90 100

EVI Value

Nu

mb

er

of

Ob

serv

ati

on

s

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METHODS - PRECIPITATION ANALYSIS

Precipitation data used from TRMM(Tropical Rainfall Measurement Mission)

0.25 degree spatial resolution (~27.5 km)(Adler, 2005)

Aggregated monthly from high frequency samples

Spatially consistent

TRMM works well for average rainfall periods (< 300 mm / month) (Aragao et al, 2005)

Slightly overestimates rainfall in droughtconditions (Aragao et al, 2005)

Slightly underestimates rainfall in periods ofhigh precipitation (Aragao et al, 2005)

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Dry season pronounced in TRMM data

METHODS - PRECIPITATION ANALYSIS

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Site 1: EVI vs. PPT, Lat. Transect (2001 - 2006)

0

50

100

150

200

250

300

350

400

450

J F M A M J J A S O N D

MonthPPT (

mm

)

0

10

20

30

40

50

60

PPT (mm)EVI (0 - 100)

Site 5: EVI vs. PPT, Lat. Transect (2001 - 2006)

0

50

100

150

200

250

300

350

400

450


Month

PPT (

mm

)

0

10

20

30

40

50

60

PPT (mm)EVI (0 - 100)

RESULTS - SYSTEM CHARACTERIZATION

Latitudinal transect comparison

Average values per month calculated from 2001 to 2005

Site 5 (near highway) showed greater dry season reduction in precipitation and EVI level

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Site 5: EVI vs. PPT, Lat. Transect (2001)

0

50

100

150

200

250

300

350

400

450

Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Oct-01 Nov-01 Dec-01

Month

PP

T (

mm

)

0

10

20

30

40

50

60

PPT (mm)

EVI (0 - 100)

Site 5: EVI vs. PPT, Lat. Transect (2005)

0

50

100

150

200

250

300

350

400

450

Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Oct-01 Nov-01 Dec-01

Month

PP

T (

mm

)

0

10

20

30

40

50

60

PPT (mm)

EVI (0 - 100)

RESULTS - SYSTEM CHARACTERIZATION

Latitudinal comparison of site 5 between years

2001 compared to 2005 (year of strong drought)

Precipitation flatlined in 2005 - almost no rain

EVI levels showed a longer and more pronounced reduction

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RESULTS - ECOSYSTEM CHANGE

Three classes established: forest, transition, savanna (non forest)

Spatial comparison between 2001 and 2006

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Ecosystem Change in Mato Gross from 2001 to 2006

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Savannah toForest

Savannah toTransition

Forest toTransition

Forest toSavannah

Change Type

Ob

serv

ed

Ch

an

ges

RESULTS - ECOSYSTEM CHANGE

State changes quantified

Forest to Savannah shift clearly dominate between 2001 to 2006

Validation in progress

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Fire Activity - 2001 and 2005, Mato Grosso

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000


Month

Fir

e F

req

uen

cy

Fire Frequency - 2001

Fire Frequency - 2005

RESULTS - FIRE FREQUENCY

MODIS Thermal anomaly data used to assess fire presence (aka hot pixels)

MOD14A2 8 day composites were aggregated over each year per pixel

1km spatial resolution

Common annual pattern showing increasedforest fires in the dry season

2005 showed a significantly greater amountof fires in part cause by the drought

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Spatial pattern of fire presence examined

Key question is to determine influence ofhuman activities on fire presence

Relationship between distance from highway and fire presence

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Fire Activity vs. Distance From Highway, Mato Grosso

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0-5km from road 5-25km from road 25-50km from road 50-75km from road More than 75km fromroad

Distance from Highway (km)

Fir

e F

req

uen

cy (

Fir

e/

km

2)

Fire Frequency


Reverse pattern to what is generally expected

Currently examining analytical method (buffer choice)

There may be the influence of smallerroad networks that have extended overtime

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CONCLUSIONS

Remote Sensing can be an effective toolfor monitoring ecosystem resilience

Different disturbances may have confounding effects that are difficult tode-couple

There appears to be a reduction in the amount of rain forest from 2001 to 2006

Combined effects of pronounced droughts influenced by El Nino and Southern AtlanticSea surface temperature anomaly with land use issues indicates these ecosystemsare being weakened

The possibility of wide-scale shift from rain forestto savanna must be further examined with field data, remote sensing, and process models

DEDesign ‣ Multimedia and Film

‣ Expositions / Spatial‣ Systems and Processes



Design, Environment and Business Evolution

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Design, Environment, and Business Evolution

PORTFOLIO HIGHLIGHTS: EXPO 2000 - HUMANKIND, NATURE, TECHNOLOGY

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PORTFOLIO HIGHLIGHTS: SULTAN BIN ABDULAZIZ SCIENCE AND TECHNOLOGY CENTER

Total size: 300,000 square feet

43 computer interactive programs42 videos161 Physical Interactive exhibits950 Graphic PanelsProduced in Arabic and English

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PORTFOLIO HIGHLIGHTS: SULTAN BIN ABDULAZIZ SCIENCE AND TECHNOLOGY CENTER

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PORTFOLIO HIGHLIGHTS: HR MACMILLAN SPACE CENTER - SCHEMATIC DESIGN

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BUSINESS EVOLUTION - ENVIRONMENTAL MANAGEMENT

-Redesign of convention business practice

-Emerging market of Environmental Management

-Three-fold approach to EM (Energy, Materials, Environmental Health)

-Small and medium sized enterprises want Green

-This is a DESIGN problem

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Support and Collaboration

Dr. Garry PetersonDr. Jonathan SeaquistProfessor Mario BungeDr. Charles LinDr. Carlos NobreLBA Research Community

McGill School of EnvironmentJ.W. McConnell Fellowship forEnvironmental Research

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POTENTIAL PROJECTS WITH IISD:

1. International Collaborative Study on Avoided Deforestation, Climate Change, and Indigenous Rights in Canada and Brazil

- Prof. Gisele Ferreira Araujo (Professor of Law, University of Sao Paolo)- IISD- United Nations

2. Application of Remote Sensing to estimating ecosystem services and “ecosystem health”

3. Analysis of advanced multimedia communication tools for communication of Sustainable Development principles

4. Assessment of Environmental Management and the Medical Industry

-DE-IISD-Selected Industrial Partner(s)-Other institutional partners

5. IISD as an institutional partner on MyEarthInteractive.com

de presentation to iisd on may 7, 2008. title: amazon resilience and business evolution

Design

remote sensing presentation

sustainable development

remote sensing social

ecosystems van

remote sensing time

beisner et

change challengesroberts

atmosphere coupling