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• W2033271074 abstract "The extensive distribution of bamboo forests in South and Southeast Asia plays an important role in the global carbon budget. It is an urgent task to accurately and in good time estimate carbon stock within these areas. In this study, linear regression, partial least-squares (PLS) regression and backpropagation artificial neural network (BP-ANN) with a Gaussian error function as the activation function of the hidden layers (Erf-BP) were used to estimate aboveground carbon (AGC) stock of Moso bamboo in Anji, Zhejiang Province, China. Based on the combined use of Landsat Thematic Mapper (TM) and field measurements, the results indicate that the Erf-BP model provided the best estimation performance, and the linear regression model performed the poorest. This study indicates that remote sensing is an effective way of estimating AGC of Moso bamboo in a large area." @default.
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• W2033271074 date "2011-03-16" @default.
• W2033271074 modified "2023-10-17" @default.
• W2033271074 title "Estimation of aboveground carbon stock of <i>Moso</i> bamboo (<i>Phyllostachys heterocycla</i> var. <i>pubescens)</i> forest with a Landsat Thematic Mapper image" @default.
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• W2033271074 doi "https://doi.org/10.1080/01431160903551389" @default.
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