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. Issue 3 (4th May 2017)

• Record Type: Journal Article
• Authors: Pham, Tien Dat; Yoshino, Kunihiko; Bui, Dieu Tien
• Is Part Of: GIScience & remote sensing. Volume 54:Issue 3(2017)
• Page Start: 329
• Page End: 353
• Publication Date: 2017-05-04
• Subjects: multi-layer perceptron neural networks -- ALOS-2 PALSAR -- biomass -- Hai Phong -- Sonneratia caseolaris; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2016.1269869
• Languages: English
• ISSNs: 1548-1603

Abstract

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 ( R 2 ), 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.