Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand

Kitsanai Charoenjit; Pierpaolo Zuddas; Pascal Allemand; Sura Pattanakiat; Katavut Pachana

doi:10.1117/1.JRS.9.096072

19 March 2015 Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand

Kitsanai Charoenjit, Pierpaolo Zuddas, Pascal Allemand, Sura Pattanakiat, Katavut Pachana

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Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096072 (March 2015). https://doi.org/10.1117/1.JRS.9.096072

Abstract

This paper deals with the efficiency of measurements of carbon stock by remote sensing techniques on Para rubber plantations in Thailand. These plantations could play an important role in carbon budget and thus are part of the Clean Development Mechanism of the Kyoto Protocol. Current methods of carbon stock estimations use middle resolution images and produce results with a large uncertainty. We use very high resolution images from the Thaichote satellite, associated with field measurements to estimate the carbon stock and its evolution in the Mae num Prasae watershed, Eastern Thailand. Using object-based classifications, the plantations have been mapped and their age has been estimated from a parametric model derived from both spectral and textural information and field data. The total biomass and carbon stocked are 2.23 and 0.99 Megaton with an uncertainty of 11%. One hundred and twenty one tons of carbon are sequestered annually in the Para rubber plantations of the studied area.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Kitsanai Charoenjit, Pierpaolo Zuddas, Pascal Allemand, Sura Pattanakiat, and Katavut Pachana "Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand," Journal of Applied Remote Sensing 9(1), 096072 (19 March 2015). https://doi.org/10.1117/1.JRS.9.096072

Published: 19 March 2015

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