Prediction of Total Iron Content in Dalbergia odorifera Leaves Based on Vegetation Index and Multispectral Texture Parameters

【Objective】This paper proposed a prediction method of total iron content( TIC) in Dalbergia odorifera leaves based on vegetation index and multi-spectral texture features,in order to provide an alternative approach for the diagnosis of heavy metal nutrition of precious tree species.【Method】In this paper,D. odorifera saplings were subjected to four levels( CK,F1,F2,F3) of iron treatment. At the end of treatment,the leaves were collected,and multi-spectral images were obtained,from which vegetation indexes and texture parameters,such as texture parameters mean value( TFMV) and texture parameters variance( TFV),were extracted and calculated,and the relationship between the variables and TIC was analyzed. The variables significantly correlated with TFC at 0.05 and 0.01 levels were screened out by significance test. Then,correlation analysis( CA),principal component analysis( PCA),average impact value( MIV) and genetic algorithm( GA) were used for secondary screening. The results were used as input variables of pa