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Title: Integration of Social and Environmental Data Using Geographic Information Systems and Remote Sensing


1
Integration of Social and Environmental Data
Using Geographic Information Systems and Remote
Sensing Techniques
  • Tom Evans
  • Department of Geography
  • Center for the Study of Institutions, Population
    and Environmental Change (CIPEC)
  • Indiana University
  • Email evans_at_indiana.edu

2
Land Cover Change
  • Changes in land cover have a variety of
    implications for ecosystem processes and human
    welfare
  • Carbon sequestration, species habitat, soil
    conservation, hydrology, charcoal,
    pharmaceuticals

3
Drivers of Land Cover Change
  • Both environmental and social factors contribute
    to changes in forest cover
  • Key challenge is to disentangle these diverse
    drivers to identify under what conditions forests
    degrade and under what conditions forests persist
    or recover
  • Mitigate factors contributing to forest
    degradation

4
Detecting Land Cover Change
  • Remote Sensing
  • Airborne, Satellite

5
Remote sensing
  • Satellites measure reflectance in multiple bands
    that cover different parts of the electromagnetic
    spectrum

6
Spectral Signatures
  • Different land cover features have different
    reflectances
  • Produce specific signatures that allow land cover
    types to be discriminated given remote sensed
    data that can detect those specific wavelengths

7
Multi-band imagery
  • Reflectances for different wavelength ranges
    collected in an image stack

8
Image sensors
  • Spatial vs. spectral resolution
  • Landsat TM 30 m (7 bands)
  • MODIS 1000 m (hyperspectral)
  • IKONOS 1 m (1 band) or 4m (4 bands)
  • High resolution data not always good
  • Computation time
  • Canopy shadow

9
Digital Orthophoto vs. Landsat TM
10
Historical aerial photography vs. Landsat TM
11
Image processing
  • Raw images must be processed to be integrated
    with other spatial data in a GIS
  • Geometric correction
  • Radiometric correction
  • Topographic correction
  • No automated way to derive land cover classes
    from remotely sensed data

12
Remote sensing data
  • Vegetation indices
  • Normalized Difference Vegetation Index

13
Ground truth data
  • GPS data used to collect point data with
    associated vegetation characteristics
  • DBH
  • Canopy height
  • Species
  • Understory

14
Image classification
  • Ground truth data used to either
  • Train classification of satellite imagery
    (supervised classification)
  • Associate land cover categories to unsupervised
    classifications

15
Geographic Information Systems
  • GIS spatial data overlay and analysis
  • John Snow and London cholera outbreak

16
Spatial data overlay
  • Given common spatial reference system (coordinate
    system, map projection, datum) data can be
    overlayed to explore spatial relationships

17
Drivers of Land Cover Change
  • Social drivers of land cover change
  • Actors and decisions
  • Individuals
  • Households
  • Communities
  • Governmental (municipal, state, federal,
    military)
  • NGO land trusts

18
Representing Spatially Explicit Land Use
Decisions
  • Cells are allocated to specific households
  • Each cell has land suitability attributes

19
Agents and Land Use Decision-Making
  • Why might two different actors choose different
    decisions for similar landscape portfolios?
  • Age, occupation
  • Preferences
  • Past Experience
  • Risk, uncertainty
  • Learning, information, decision-making

20
Spatial Metrics
  • Spatial descriptors that describe ecological form
    and function
  • Can be measured at different spatial units

21
Linking agents to Land Partitions
22
Characteristics of Agent-Based Models
  • Micro-level behaviors, macro-level outcomes
  • Simple behaviors by individual agents lead to
    complex macro-scale outcomes
  • Ant colony
  • Bird flock
  • Simple individual level behaviors ? macro
    outcomes
  • Households as actors in complex systems

Bird Flock Demo
23
Land Cover Change in Montane Mainland Southeast
Asia
  • Montane Mainland Southeast Asia
  • Northern Thailand, Laos, Southern China
  • Poor infrastructure (roads)
  • Major north-south corridor highway just completed
  • Heterogeneous land suitability (especially
    topography)
  • Considerable constraints to large scale crop
    production

24
Agent based models of land cover change
  • Actors (households) with specific actions
  • Allocate labor to land in different uses
  • Crops or rubber plantation
  • Interactions - observation
  • Heterogeneity diverse preferences

25
Land Cover Change in Montane Mainland Southeast
Asia
  • Major land use transition occurring
  • Shifting cultivation ? rubber plantations
  • Shifting cultivation (slash and burn)
  • Portion of landscape in moderate diversity,
    successional forest state
  • Rubber plantations
  • Mono-crop, low ecological diversity
  • Traditional system to commodity market system
  • Considerable environmental impacts
  • Carbon sequestration
  • Hydrological dynamics
  • Species habitat, non-timber forest products

26
Rubber Production
27
Land Cover Change in MMSEA
  • Indonesia and China major producers, China
    started developing rubber industry in 1950s with
    ramp up in 1980s
  • Rubber production now moving into Laos and
    Thailand

28
Agricultural Production vs. Agroforestry
  • Shifting cultivation provides annual income from
    year planted
  • Modest but consistent harvests and returns
  • 2-3 years of yield
  • Rubber production
  • 7 years before trees can be tapped, latex
    harvested
  • 30 years of yield
  • New technical skills needed to develop plantation
  • Considerable risk to taking land out of
    production for such a long period of time

29
Rate of adoption and adopter categories
  • Innovators
  • Early adopters
  • Early majority
  • Late majority
  • Laggards

30
High relative price of rubber
31
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32
Lomue agent-based model
  • Model of land cover change, emphasis on adoption
    of rubber by households 1984-present
  • Key dynamics explored in model
  • Why are some villagers early adopters and why are
    some late adopters?
  • What is the impact of this agricultural
    transition on household inequality?
  • Do some households lose out over time?

33
Utility Maximization Approach
  • Assumes agents allocate labor to the option that
    will return the greatest utility
  • Bounded rationality vs. satisficing behavior…

U Utility of option A Alpha preference
parameter, fit in model calibration P Price or
revenue returned from option A L Labor
allocated to choice A M number of cells, or
number of units allocated C Cost of expending
labor for option A Y Yield/unit area of option
A R Risk parameter (time horizon)
Evans et al. 2004, Manson and Evans 2007, Evans
et al. 2008
34
Lomue ABM
  • Temporal extent - 1984-present
  • Models household adoption of rubber
  • Expected utility calculation based on crop and
    rubber prices
  • Risk parameter (adopter category)
  • Agent specific observation windows set of
    other agents of which a particular agent is aware
  • Cell based land suitability
  • Accessibility, topography, zoning, positive
    spatial externality (rubber)

35
Lomue ABM
  • Model fitting
  • Village level spatial composition, spatial
    pattern
  • HH level data
  • Date of first rubber planting
  • Number of ha in rubber over time
  • Model output
  • Land cover
  • Gini coefficient of HH level income

36
Lomue Model - 1984
  • Initial village settlement

37
Lomue Model - 1994
  • Spatially distributed shifting cultivation, first
    initial rubber plantings

38
Lomue Model - 2001
  • Early adopters harvesting rubber

39
Lomue Model - 2005
  • High HH inequality, major rubber expansion

40
Model Dynamics
  • Initially slow adoption of rubber (1984-1997)
  • After 7-8 years, more rapid adoption
  • Households observe success of early adopters
  • Able to reproduce theoretical categories of
    adoption/diffusion literature
  • By 2005 majority of households have at least some
    land allocated to rubber

41
Contact evans_at_indiana.edu
Acknowledgements NSF Global Land
Project CIPEC staff and collaborators
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