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

Tien Dat Pham*, Kunihiko Yoshino, Dieu Tien Bui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)329-353
Number of pages25
JournalGIScience and Remote Sensing
Volume54
Issue number3
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • multi-layer perceptron neural networks
  • ALOS-2 PALSAR
  • biomass
  • Hai Phong
  • Sonneratia caseolaris

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