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Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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Title: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data


1
Mapping Understory Vegetation Using Phenological
Characteristics Derived from Remotely Sensed Data
  • Mao-Ning Tuanmu1, Andrés Viña1, Scott Bearer2,
  • Weihua Xu3, Zhiyun Ouyang3, Hemin Zhang4 and
    Jianguo (Jack) Liu1
  • 1 Michigan State University
  • 2 The Nature Conservancy
  • 3 Chinese Academy of Sciences
  • 4 Wolong Nature Reserve, China

2
Understory Vegetation
  • An important component in forest ecosystems
  • Affecting forest structure, function and species
    composition
  • Supporting wildlife species
  • Providing ecosystem services
  • Lack of detailed information on its
  • spatio-temporal dynamics
  • Interference of overstory canopy on the remote
    detection of understory vegetation
  • Limitations of LANDSAT data and LiDAR data

3
Land Surface Phenology
  • Seasonal pattern of variation of vegetated land
    surfaces captured by remotely sensed data
  • Affected by both overstory and understory
    vegetation

http//landportal.gsfc.nasa.gov/Documents/ESDR/Phe
nology_Friedl_whitepaper.pdf
4
Objectives
  • To develop an effective remote sensing approach
    using land surface phenologies for mapping
    overall understory vegetation
  • To explore the application of this approach to
    mapping and differentiating individual understory
    species

5
Methods
6
Wolong Nature Reserve
  • 2000 km2
  • 10 of entire wild giant panda population
  • Evergreen bamboo species dominate the understory
    of forests
  • Two dominant bamboo species constitute the major
    food for giant pandas

7
Arrow and Umbrella Bamboo
Umbrella bamboo
  • Arrow bamboo
  • Bashania fangiana
  • Elevation 2300 3600 m
  • Umbrella bamboo
  • Fargesia robusta
  • Elevation 1600 2650 m

Photographed by Andrés Viña (Elevation 2546 m)
Arrow bamboo
8
Phenology Metrics
  • Time series of 16-day MODIS-WDRVI composites
  • MODIS surface reflectance ( 250 m/pixel)
  • Wide Dynamic Range Vegetation Index (WDRVI)
  • Eleven phenology metrics

A - Base level B - Maximum level C Amplitude D
- Date of start of a season E - Date of middle of
a season F - Date of end of a season G - Length
of a season H - Large integral I - Small
integral J - Increase rate K - Decrease rate
9
Identifying Phenological Features of Forests with
Understory Bamboo
  • Comparing the 11 phenology metrics among 5 groups
    of pixels
  • Pixels in the entire study area (background
    pixels)
  • Pixels with forest cover
  • Forest pixels with understory bamboo
  • Forest pixels with arrow bamboo
  • Forest pixels with umbrella bamboo

10
Overall Bamboo Distribution Model
  • Maximum Entropy Algorithm (MAXENT)
  • Using pixels with understory bamboo cover 25
    as presence locations
  • Using the 11 phenology metrics as predictor
    variables
  • Estimating bamboo presence probability (01)
    across the entire study area
  • Model evaluation
  • Kappa statistics
  • Area under the receiver operating characteristic
    curve (AUC)

11
Individual Bamboo Distribution Model
  • Using pixels with arrow and umbrella bamboo as
    presence locations, separately
  • Using the 11 phenology metrics as predictor
    variables
  • Using elevation as an additional predictor
    variable
  • Comparing the accuracy between the models with
    and without elevation

12
Results
13
Overall Bamboo Distribution
  • Kappa 0.5910.018
  • AUC 0.8510.005

14
Phenological Features of Forests with Understory
Bamboo
  • Pixels with overall understory bamboo were
    significantly different from background and
    forest pixels in most phenology metrics
  • Pixels with single bamboo species (arrow or
    umbrella bamboo) were also different from the
    background and forest pixels in most metrics

15
Individual Bamboo Distribution
Kappa 0.68 0.02 AUC 0.91 0.01
Kappa 0.46 0.02 AUC 0.80 0.01
Kappa 0.66 0.02 AUC 0.90 0.01
Kappa 0.70 0.02 AUC 0.92 0.01
16
Summary
  • Phenology metrics derived from a time series of
    MODIS data can be used to distinguish forests
    with understory bamboo from other land cover
    types
  • By combining field data, phenology metrics, and
    maximum entropy modeling, understory bamboo can
    be mapped with high accuracy
  • By incorporating species-specific information
    (e.g., elevation), individual understory species
    can be differentiated

17
Advantages of the Approach
  • Suitability for broad-scale monitoring
  • Easy access, global coverage, and temporally
    continuous availability of MODIS data
  • Generality
  • Without the need of specific information on the
    phenological difference between overstory and
    understory vegetation or the relationships
    between understory vegetation and environmental
    variables
  • Flexibility and extensibility
  • Overall understory vegetation or groups of
    species with similar phenological characteristics
  • Individual species within specific geographic
    areas

18
Conservation Implications
  • Ecosystem management
  • Invasive understory species
  • Biodiversity conservation
  • Biodiversity of understory vegetation
  • Wildlife conservation and habitat management
  • Habitat quality
  • Habitat monitoring

19
Acknowledgements
  • National Aeronautics and Space Administration
  • National Science Foundation
  • Michigan Agricultural Experiment Station
  • National Natural Science Foundation of China

20
Reference
  • Remote Sensing of Environment (doi10.1016/j.rse.
    2010.03.008 )
  • http//www.csis.msu.edu/Publications/

21
International Network of Research on Coupled
Human and Natural Systems (CHANS-Net) Sponsored
by The National Science FoundationCoordina
torsJianguo (Jack) Liu and Bill McConnell
22
Advisory Board
  • Stephen Carpenter (University of Wisconsin at
    Madison)
  • William Clark (Harvard University)
  • Ruth DeFries (Columbia University)
  • Thomas Dietz (Michigan State University)
  • Carl Folke (Stockholm University, Sweden)
  • Simon Levin (Princeton University)
  • Elinor Ostrom (Indiana University)
  • Billie Lee Turner II (Arizona State University)
  • Brian Walker (Commonwealth Scientific and
    Industrial Research Organization, Australia)

23
Objectives of CHANS-Net
  • Promote communication and collaboration across
    the CHANS community.
  • Generate and disseminate comparative and
    synthesis scholarship on CHANS.
  • Expand the CHANS community.

24
Example Activities of CHANS-Net
25
CHANS Workshops
  • First Workshop
  • Challenges and Opportunities in Research on
    Complexity of Coupled Human and Natural Systems
  • at the 2009 conference of US-IALE

26
CHANS Symposia
  • 2009 Conference of US-IALE (US Regional
    Association, International Association for
    Landscape Ecology)
  • 2010 Conference of AAG (Association of American
    Geographers)
  • 2010 National Science Foundation
  • 2011 Conference of AAAS (American Association for
    the Advancement of Science)

27
CHANS Fellows Program
  • Opportunities for junior scholars interested in
    CHANS to attend relevant meetings, symposia, and
    workshops.
  • CHANS Fellows
  • 14 at the 2009 US-IALE meeting
  • 10 at the 2010 US-IALE meeting
  • 10 at the 2010 AAG meeting

28
Web-based Resource Center (www.CHANS-Net.org)
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