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

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
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 (0~1) 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: 0.591±0.018
AUC: 0.851±0.005

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:
0.46 ± 0.02
AUC:
0.80 ± 0.01
Kappa:
0.68 ± 0.02
AUC:
0.91 ± 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 (doi: /j.rse )

21. International Network of Research on Coupled Human and Natural Systems (CHANS-Net) Sponsored by The National Science Foundation Coordinators Jianguo (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)