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Estimating Leaf Area Index of Rubber Tree Plantation Using Worldview-2 Imagery

Werapong Koedsin and Kanjana Yasen
Faculty of Technology and Environment, Prince of Songkla University Phuket Campus, Kathu, Phuket, Thailand

Abstract—Leaf Area Index (LAI) is a crucial biophysical characteristic of vegetation is directly related to the yield, energy balance of the land surface, hydrology and climate system, ecosystem productivity models, power exchange with the atmosphere and carbon cycle model. For efficiently, fast and accurate LAI mapping of rubber tree are very importance. Therefore, this study aimed to estimation the LAI of the rubber tree using WorldView-2 imagery. The 8 spectral bands from WorldView-2 satellite image were used as input variables of Stepwise Multiple Linear Regression and Artificial Neural Networks for estimate the LAI of the rubber tree at Paklok sub-district, Thalang district, Phuket Province, Thailand. The results showed that Artificial Neural Networks provide the most accurate (Root Mean Square Error (RMSE) = 0.31) when compared with Stepwise Multiple Linear Regression (RMSE = 0.49). We hope that the methodology presented in this study can be used as a guideline for study in other area and for rubber tree plantation management or predictions the rubber yield in the future.

Index Terms—rubber tree, leaf area index, remote sensing, stepwise multiple linear regression, artificial neural networks

Cite: Werapong Koedsin and Kanjana Yasen, "Estimating Leaf Area Index of Rubber Tree Plantation Using Worldview-2 Imagery," Journal of Life Sciences and Technologies, vol. 4, no. 1, pp. 1-6, June 2016. doi: 10.18178/jolst.4.1.1-6
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