Mapping Understory Vegetation Using Land Surface Phenology
Mao-Ning
Tuanmu, Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, tuanmuma@msu.edu
Andrés
Viña, Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, vina@msu.edu
Scott
Bearer, The Nature Conservancy, Williamsport Field Office, sbearer@tnc.org
Weihua
Xu, State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, xuweihua123@gmail.com
Zhiyun
Ouyang, State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, zyouyang@rcees.ac.cn
Hemin
Zhang, China Conservation and Research Center for the Giant Panda, Wolong Nature Reserve, wolong_zhm@126.com
Jianguo
Liu, Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, jliu@panda.msu.edu
(Presenting)
Understory vegetation is an important component in forest ecosystems. Understanding its spatio-temporal dynamics is essential for management and conservation. However, detailed information on the distribution of understory vegetation across large spatial extents is usually unavailable, due to the interference of overstory canopy on the remote detection of understory vegetation. While many efforts have been made to overcome this challenge, mapping understory vegetation across large spatial extents is still limited due to a lack of generality of the developed methods and limited availability of required remotely sensed data. In this study, we used understory bamboo in Wolong Nature Reserve, China as a case study to develop and test an effective and practical remote sensing approach for mapping understory vegetation. Using phenology metrics generated from a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data, we characterized the phenological features of forests with understory bamboo. Using maximum entropy modeling together with these phenology metrics, we successfully mapped the spatial distribution of all understory bamboo species combined (kappa: 0.59; AUC: 0.85). In addition, by incorporating elevation information we further mapped the distribution of each of two individual bamboo species, Bashania faberi and Fargesia robusta (kappa: 0.68 and 0.70; AUC: 0.91 and 0.92, respectively). Due to its generality, flexibility and extensibility, this approach constitutes an improvement to the remote detection of understory vegetation, making it suitable for mapping different understory species in different geographic settings. Both biodiversity conservation and wildlife habitat management may benefit from the detailed information on understory vegetation across large areas through the applications of this approach.
Presentation Type: Poster
Poster Session: Ecosystems Science
NASA TE Funded Awards Represented:
NONE: Related Activity or Previously Funded TE Award