Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/115445
Title: Integrating ALOS PALSAR and spot satellite imagery for tropical forest biomass estimation
Authors: Goh, J.Y.
Miettinen, J. 
Chia, A.S. 
Liew, S.C. 
Kwoh, L.K. 
Keywords: ALOS PALSAR
Biomass estimation
Humid tropical forest
Singapore
Issue Date: 2011
Citation: Goh, J.Y., Miettinen, J., Chia, A.S., Liew, S.C., Kwoh, L.K. (2011). Integrating ALOS PALSAR and spot satellite imagery for tropical forest biomass estimation. 32nd Asian Conference on Remote Sensing 2011, ACRS 2011 2 : 906-911. ScholarBank@NUS Repository.
Abstract: Due to the increasing interest in the amount of carbon stored in forest ecosystems and growing demand for accurate monitoring of carbon fluxes between vegetation and atmosphere for the purposes of the REDD+ scheme, remote sensing based methods are needed for forest biomass estimation. In this study we investigate the usability of a combination of SPOT 5 optical satellite imagery and ALOS PALSAR data for above ground biomass estimation in humid tropical forest. The study area covered around 2000 ha in the Central Nature Reserve of Singapore which is a protected area of humid tropical evergreen forests. Biomass was measured in 25 field plots and linear regression models were developed between ground biomass and remotely sensed parameters from SPOT 5 HRG and ALOS PALSAR satellite data using stepwise regression approach. Accuracy of the models was evaluated using an independent set of 10 field sample plots. The model considered to be most suitable for practical use which included NIR band from SPOT 5 and HV radar backscatter from ALOS PALSAR achieved adjusted r 2 of 0.46 and RMSE of 152 t/ha (36%) with essentially no bias. Based on the independent validation plots the model underestimated the average biomass of the plots by only 1%. Thereby, the results suggest that a combination of optical and radar remote sensing data can be used to produce reliable biomass estimates for large areas of humid tropical forests using empirical regression models in a rather homogeneous environment. However, the results also show that pixel level errors of the models may be large and that the use of mere optical data may enable similar level of results. Furthermore, the study highlights the unsuitability of empirical models for biomass estimation outside the vegetation type they have been developed for.
Source Title: 32nd Asian Conference on Remote Sensing 2011, ACRS 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/115445
ISBN: 9781618394972
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