Geoinformatics Unit

Tien Dat PHAM

     

Current Position

Tien Dat Pham is a postdoctoral researcher at Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Japan.
His interests include optical and SAR remote sensing applications, land-use/land-cover change analysis, forest biomass and carbon stocks estimation using SAR, and optical data, especially for mangrove forests in the tropics.

Biography

2018 May - Present    Postdoctoral Researcher, RIKEN AIP, Japan
2015 Apr - 2018 Mar    Ph.D. in Policy and Planning Sciences, The University of Tsukuba, Japan
2012 Aug - 2014 Sep    Research fellow, Center for Agricultural Researcher and Ecological Studies, Vietnam National University of Agriculture, Vietnam
2010 Aug - 2012 Jul    M.Sc. in Environmental Sciences, The University of Tsukuba, Japan

Journal Papers

  1. T. D. Pham, N. Yokoya, D. T. Bui, K. Yoshino, and D. A. Friess, " Remote sensing approaches for monitoring mangrove species, structure and biomass: opportunities and challenges ," Remote Sensing, vol. 11, no. 3, pp. 230, 2019.
    PDF    Quick Abstract

    Abstract: The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.