Title: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data
1Mapping 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
2Understory 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
3Land 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
4Objectives
- 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
5Methods
6Wolong 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
7Arrow 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
8Phenology 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
9Identifying 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
10Overall 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)
11Individual 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
12Results
13Overall Bamboo Distribution
14Phenological 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
15Individual 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
16Summary
- 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
17Advantages 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
18Conservation Implications
- Ecosystem management
- Invasive understory species
- Biodiversity conservation
- Biodiversity of understory vegetation
- Wildlife conservation and habitat management
- Habitat quality
- Habitat monitoring
19Acknowledgements
- National Aeronautics and Space Administration
- National Science Foundation
- Michigan Agricultural Experiment Station
- National Natural Science Foundation of China
20Reference
- Remote Sensing of Environment (doi10.1016/j.rse.
2010.03.008 ) - http//www.csis.msu.edu/Publications/
21International Network of Research on Coupled
Human and Natural Systems (CHANS-Net) Sponsored
by The National Science FoundationCoordina
torsJianguo (Jack) Liu and Bill McConnell
22Advisory 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)
23Objectives of CHANS-Net
- Promote communication and collaboration across
the CHANS community. - Generate and disseminate comparative and
synthesis scholarship on CHANS. - Expand the CHANS community.
24Example Activities of CHANS-Net
25CHANS Workshops
- First Workshop
- Challenges and Opportunities in Research on
Complexity of Coupled Human and Natural Systems - at the 2009 conference of US-IALE
26CHANS 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) -
27CHANS 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
28Web-based Resource Center (www.CHANS-Net.org)