Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks |
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| Authors: | Tien Dat Pham Kunihiko Yoshino Dieu Tien Bui |
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| Institution: | 1. Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba City, Ibaraki 305-8573, Japan;2. Center for Agricultural Research and Ecological Studies (CARES), Vietnam National University of Agriculture, Trau Quy, Gia Lam, Hanoi, Vietnamdat6784@gmail.com tiendat@cares.org.vn;4. Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba City, Ibaraki 305-8573, Japan;5. Geographic Information System Group, Department of Business Administration and Computer Science, University College of Southeast Norway, Hallvard Eikas Plass 1, B? i Telemark N-3800, Norway |
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| Abstract: | This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R2), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha?1 (average = 55.8 Mg ha?1); below-ground biomass ranged between 4.06 and 436.47 Mg ha?1 (average = 81.47 Mg ha?1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha?1 (average = 64.52 Mg C ha?1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas. |
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| Keywords: | multi-layer perceptron neural networks ALOS-2 PALSAR biomass Hai Phong Sonneratia caseolaris |
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