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
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.
Title:
| 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 | |
| Authors: | Pham, T.D. Yoshino, K. Bui, D.T. |
| Keywords: | ALOS-2 PALSAR Biomass Hai Phong multi-layer perceptron neural networks Sonneratia caseolaris |
| Issue Date: | 2016 |
| Publisher: | Taylor and Francis Inc. |
| 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. |
| Description: | GIScience and Remote Sensing Volume 54, Issue 3, 4 May 2017, Pages 329-353 |
| URI: | http://www.tandfonline.com/doi/abs/10.1080/15481603.2016.1269869?journalCode=tgrs20 http://repository.vnu.edu.vn/handle/VNU_123/32555 |
| ISSN: | 15481603 |
| Appears in Collections: | Bài báo của ĐHQGHN trong Scopus |
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