Estimation of Gross Primary Production using Data mining approach
The application in Northeast Thailand using an Artificial Neural Networks method
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The purpose of this book is to improve our understanding of the role of terrestrial vegetation in carbon cycle and its variation under climate variability. A combination between meteorological and remote sensing data using various techniques and software were employed to identify and forecast change in Gross Primary Production (GPP). The vegetation greenness and GPP were obtained from remote sensing data because of the advantages in terms of continuous monitoring and cover the various land cover types. The method used for this research included spatial interpolation, digital image processing, ...
The purpose of this book is to improve our understanding of the role of terrestrial vegetation in carbon cycle and its variation under climate variability. A combination between meteorological and remote sensing data using various techniques and software were employed to identify and forecast change in Gross Primary Production (GPP). The vegetation greenness and GPP were obtained from remote sensing data because of the advantages in terms of continuous monitoring and cover the various land cover types. The method used for this research included spatial interpolation, digital image processing, correlation analysis and Artificial Neural Networks (ANNs) for data preparation, analysis and forecasting. This book revealed the advantages of using GPP obtained from the satellite data for continuous monitoring carbon fixing by vegetation. The integration of meteorological and satellite data with the ANNs technique can be used as an alternative method to estimate GPP where the carbon fluxes data from the towers at specific sites is limited.
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