Combining Multi-Indices by Neural Network Model for Estimating Canopy Chlorophyll Content: a Case Study of Interspecies Competition between <i>Spartina alterniflora</i> and <i>Phragmites australis</i>

Abstract

The invasive species Spartina alterniflora show a significant coexistence zonation pattern with local Phragmites australis in different mixture ratio, increasing the difficulty to monitor their distribution directly by remote sensing. Canopy chlorophyll content (CCC) is an important indicator to monitor the growth and physiological status. The objective of this study was to estimate CCC under different mixture ratio. Five spectral indices were selected and combined via back propagation (BP) neural network model for estimating CCC. Combining multi-indices yielded better results (R 2 = 0.7729, RMSE = 53.01 ug.cm -2 ) on average than the best single spectral index (R 2 = 0.7190, RMSE = 63.53 ug.cm -2 ) without distinguishing interspecies competition, with a total increase of 7.5% in the R 2 and a decrease of 16.56% in the RMSE. Meanwhile, when considering interspecies competition, the estimating results obtained by the BP neural network model achieved a further improvement of the R 2 value, ranging from 3.57% to 20.37%, while the prediction error reduced at varying degrees (maximum reduction of 23.78%). The results indicate that combining multi-indices by BP neural network model can alleviate the influence of interspecies competition and achieve higher estimating accuracy.

Construction of customer-aware grid interactive service system based on neural network model

“…Despite the considerable progress, the literature [6] points out that neither single feedforward nor recurrent neural network models can achieve satisfactory results for production processes with complex characteristics. In the literature [7], in recent years, it was found that deep learning spatio-temporal fusion soft measurement modeling that considers both temporal and spatial feature information can achieve improved measurement accuracy.…”