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Remote Sens. 2015, 7(9), 12192-12214;

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Center of Integrated Geographic Information Analysis, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA
Guangdong Provincial Key Laboratory of Geological Processes and Mineral Resources Survey, School of Earth Science and Geological Engineering, Sun Yat-sen University, Guangzhou 510275, China
Authors to whom correspondence should be addressed.
Academic Editors: Chandra Giri, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 13 July 2015 / Revised: 9 September 2015 / Accepted: 11 September 2015 / Published: 21 September 2015
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a “Weights” method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation. View Full-Text
Keywords: mangrove; vegetation biomass; species level; variable importance; BP ANN; WorldView-2 mangrove; vegetation biomass; species level; variable importance; BP ANN; WorldView-2

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhu, Y.; Liu, K.; Liu, L.; Wang, S.; Liu, H. Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images. Remote Sens. 2015, 7, 12192-12214.

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