mapping understory vegetation using phenological characteristics derived from remotely sensed data
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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 University2 The Nature Conservancy
3 Chinese Academy of Sciences4 Wolong Nature Reserve, China
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
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/Phenology_Friedl_whitepaper.pdf
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
Methods
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
Arrow and Umbrella Bamboo
• Arrow bamboo – Bashania fangiana– Elevation: 2300 – 3600 m
• Umbrella bamboo – Fargesia robusta– Elevation: 1600 – 2650 m
Arrow bamboo
Umbrella bamboo
Photographed by Andrés Viña (Elevation: 2546 m)
Phenology Metrics• Time series of 16-day MODIS-WDRVI composites
MODIS surface reflectance (~ 250 m/pixel) Wide Dynamic Range Vegetation Index (WDRVI)
• Eleven phenology metricsA - Base levelB - Maximum levelC – AmplitudeD - Date of start of a seasonE - Date of middle of a seasonF - Date of end of a seasonG - Length of a seasonH - Large integralI - Small integralJ - Increase rateK - Decrease rate
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
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)
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
Results
Overall Bamboo Distribution
• Kappa: 0.591±0.018 AUC: 0.851±0.005
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
Individual Bamboo Distribution
Kappa: 0.46 ± 0.02
AUC:0.80 ± 0.01
Kappa: 0.66 ± 0.02
AUC:0.90 ± 0.01
Kappa: 0.68 ± 0.02
AUC:0.91 ± 0.01
Kappa: 0.70 ± 0.02
AUC:0.92 ± 0.01
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
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
Conservation Implications
• Ecosystem management Invasive understory species
• Biodiversity conservation Biodiversity of understory vegetation
• Wildlife conservation and habitat management Habitat quality Habitat monitoring
Acknowledgements
• National Aeronautics and Space Administration • National Science Foundation • Michigan Agricultural Experiment Station• National Natural Science Foundation of China
Reference
• Remote Sensing of Environment (doi:10.1016/j.rse.2010.03.008 )
• http://www.csis.msu.edu/Publications/
International Network of Research on Coupled Human and Natural Systems (CHANS-Net)
Sponsored by The National Science Foundation
CoordinatorsJianguo (Jack) Liu and Bill McConnell
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)
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.
Example Activities of CHANS-Net
CHANS Workshops
First Workshop “Challenges and Opportunities in Research on
Complexity of Coupled Human and Natural Systems”
at the 2009 conference of US-IALE
CHANS Symposia2009 Conference of US-IALE (US Regional
Association, International Association for Landscape Ecology)
2010 Conference of AAG (Association of American Geographers)
2010 National Science Foundation2011 Conference of AAAS (American
Association for the Advancement of Science)
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
Web-based Resource Center (www.CHANS-Net.org)
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