Study On The Classification Of Forest Cover Types Based On The Stacking Method Of Tree Model

The classification research of forest cover type is the precondition of forest resource change monitoring,rational development and artificial restoration.Therefore,it is of great practical significance to build an accurate classification model of forest cover type.This paper improves the classification ability of the model from two aspects of model and feature.In the model,a new practical stacking method based on RF,ET,GBDT,XGBoost and LightGBM is proposed.In the aspect of features,we combine feature intersection and backward feature selection based on random forest importance to process features.From the empirical results of Roosevelt National Forest data in North Dakota,the classification accuracy of ET-RF-LR model is 89.2%,and the thirty one models after feature processing are better than those without feature processing.The optimal model in the original data is ET-XGBoost-LightGBM-LR,and the optimal model after feature processing is ET-RF-LR.Therefore,the classification model in this paper can select the corresponding optimal model according to the different data to achieve the purpose of accurate classification.In conclusion,the stacking classification model based on tree model proposed in this paper can provide valuable reference and ideas for the classification of forest cover types,and has good theoretical and practical significance.