Abstract:

High-precision estimation model of arbor canopy chlorophyll content is important to forestry and ecology. The spectral reflectance of canopy was measured by ASD FieldSpec and the chlorophyll content was measured by spectrophotometry at the same time. The sample data were pretreated by the methods of mean, smoothing and derivative, and then the red edge parameters of samples were extracted from the pretreated spectra data. The eleven red edge parameters were analyzed with principal component analysis (PCA). The anterior 7 principal components computed by PCA were used as the input variables of back-propagation artificial neural network (BP-ANN) which included one hidden layer which had four, six, eight, ten, twelve or fourteen neurons, while the chlorophyll content was used as the output variables of BP-ANN, and then the three layers BP-ANN discrimination model was built. Weight value and threshold value of this model were optimized by using genetic algorithm. The fitness between the predicted value and the measured value was tested by the determination coefficient, the lowest root mean-square error and the average relative error. The results show that the precisions of six models are all above 92.0% and the precision of the model which had ten hidden layer neurons is 97.372%. The canopy chlorophyll content of Chinese fir can be accurately estimated by using this model.

Key words: red edge parameters, GA-BP neural network, chlorophyll content, models