PCA-RF model for Dendrolimus punctatus Walker damage detection

Abstract

At the present stage, the effective coupling information of “ground-space” is still a fundamental way to detect forest pest damage rapidly and accurately in remote sensing. It is of great significance to explore and construct a detection model that can comprehensively and effectively utilize ground microscopic features and remote sensing pixel information. Taking Dendrolimus punctatus Walker damage as the study object, the host characteristics and the differences with healthy pine forest are analyzed from the aspects of leaf volume, greenness, moisture, forest form and spectrum. Four experimental sites of Sanming City, Jiangle County, Sha County and Yanping District in Nanping City, Fujian, China are set as the experimental areas, and two forest stand features of LAI and SEL are measured, the remote sensing indicators of NDVI, WET and B2, B3, B4 are calculated or extracted. The PCA-RF detection model is constructed with pest levels of non-damage, mild damage, moderate damage and severe damage as dependent variables. This model reduces the dimensions by converting the initial variables into several principal components and making the principal components input to the random forest. The first three principal components of PCA can better replace the information of the original seven characteristic indicators, thereby reducing the seven-dimensional information to three dimensions. One hundred eighty-two samples are randomly divided into the training set and test set for the five repeats. The detection accuracy, Kappa coefficient, ROC are selected to evaluate the pest damage detection effects of PCA-RF model, and compare with Fisher discriminant analysis and BP neural network. The results show that the detection accuracy, Kappa coefficient and AUC of PCA-RF model in the training set are not as good as those of FDA and BPNN, but the detection accuracy and Kappa coefficient in the test set are significantly better than the other two algorithms, and the AUC value is also significantly higher than BNPP. This study proves that PCA-RF model simplifies the complex problem, inherits the advantages of random forest, has very strong generalization ability and robustness, and can be used as an effective solution for forest pest and disease detection.