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 Tuanmu 1 , Andrés Viña 1 , Scott Bearer 2 , Weihua Xu 3 , Zhiyun Ouyang 3 , Hemin Zhang 4 and Jianguo (Jack) Liu 1 1 Michigan State University 2 The Nature Conservancy 3 Chinese Academy of Sciences 4 Wolong Nature Reserve, China

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Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data. Mao-Ning Tuanmu 1 , Andrés Viña 1 , Scott Bearer 2 , Weihua Xu 3 , Zhiyun Ouyang 3 , Hemin Zhang 4 and Jianguo (Jack) Liu 1 1 Michigan State University 2 The Nature Conservancy - PowerPoint PPT Presentation

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Page 1: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 2: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 3: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed 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

Page 4: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 5: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

Methods

Page 6: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 7: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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)

Page 8: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 9: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 10: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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)

Page 11: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 12: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

Results

Page 13: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

Overall Bamboo Distribution

• Kappa: 0.591±0.018 AUC: 0.851±0.005

Page 14: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 15: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 16: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 17: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 18: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

Conservation Implications

• Ecosystem management Invasive understory species

• Biodiversity conservation Biodiversity of understory vegetation

• Wildlife conservation and habitat management Habitat quality Habitat monitoring

Page 19: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

Acknowledgements

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

Page 20: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

Reference

• Remote Sensing of Environment (doi:10.1016/j.rse.2010.03.008 )

• http://www.csis.msu.edu/Publications/

Page 21: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

International Network of Research on Coupled Human and Natural Systems (CHANS-Net)

Sponsored by The National Science Foundation

CoordinatorsJianguo (Jack) Liu and Bill McConnell

Page 22: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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)

Page 23: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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.

Page 24: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

Example Activities of CHANS-Net

Page 25: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

CHANS Workshops

First Workshop “Challenges and Opportunities in Research on

Complexity of Coupled Human and Natural Systems”

at the 2009 conference of US-IALE

Page 26: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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)

Page 27: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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

Page 28: Mapping Understory Vegetation Using Phenological Characteristics Derived from Remotely Sensed Data

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