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Estimating chlorophyll content of spartina alterniflora at leaf level using hyper-spectral data
Author(s): Jiapeng Wang; Runhe Shi; Pudong Liu; Chao Zhang; Maosi Chen
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Paper Abstract

Spartina alterniflora, one of most successful invasive species in the world, was firstly introduced to China in 1979 to accelerate sedimentation and land formation via so-called “ecological engineering”, and it is now widely distributed in coastal saltmarshes in China. A key question is how to retrieve chlorophyll content to reflect growth status, which has important implication of potential invasiveness. In this work, an estimation model of chlorophyll content of S. alterniflora was developed based on hyper-spectral data in the Dongtan Wetland, Yangtze Estuary, China. The spectral reflectance of S. alterniflora leaves and their corresponding chlorophyll contents were measured, and then the correlation analysis and regression (i.e., linear, logarithmic, quadratic, power and exponential regression) method were established. The spectral reflectance was transformed and the feature parameters (i.e., “san bian”, “lv feng” and “hong gu”) were extracted to retrieve the chlorophyll content of S. alterniflora . The results showed that these parameters had a large correlation coefficient with chlorophyll content. On the basis of the correlation coefficient, mathematical models were established, and the models of power and exponential based on SDb had the least RMSE and larger R2 , which had a good performance regarding the inversion of chlorophyll content of S. alterniflora.

Paper Details

Date Published: 7 September 2017
PDF: 8 pages
Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 104050Y (7 September 2017); doi: 10.1117/12.2273109
Show Author Affiliations
Jiapeng Wang, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Runhe Shi, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Joint Research Institute for New Energy and the Environment (China)
Pudong Liu, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Chao Zhang, East China Normal Univ. (China)
Maosi Chen, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 10405:
Remote Sensing and Modeling of Ecosystems for Sustainability XIV
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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