Extraction of Cherry Planting Area in Yuntai Mountain Forest Farm of Lianyungang Based on Drone Images
Utilizing vegetation phenological features for vegetation information extraction is an im-portant research content in vegetation remote sensing classification.The cherry blossom season in Yuntai Mountain Forest Farm exhibits significant differences from other vegetation types,ena-bling the spectral features of cherry blossoms to be distinct and unique during their blooming and fading stages.Especially during the peak of cherry blossoms,the changes in their spectral char-acteristics provide a favorable technical means for extracting vegetation areas using remote sens-ing techniques.This study selects high-resolution RGB drone images of the cherry blossom sea-son in March 2020 as the data source and utilizes the nearest neighbour method(NNM),random forest(RF),and support vector machine(SVM)to classify and extract the cherry planting areas in Yuntai Mountain Forest Farm.Focusing on the specific vegetation type of cherry trees,this study explores remote sensing image extraction methods during the blooming season.This re-search not only fills the gap in the study of cherry tree phenological feature extraction but also provides a referential classification method for other vegetation types with unique phenological features.The research results indicate that combining the characteristics of cherry tree remote sensing images,the use of different classification methods significantly improves the classification accuracy of cherry trees,with the highest extraction accuracy reaching 98％.Accuracy evaluation analysis of the classified and extracted images reveal that the random forest method achieves the best cherry tree flower extraction quality when vegetation indices,spectral features,and texture features are added,followed by the nearest neighbour method with the addition of these features.
cherry tree extractionphenological characteristicscomparative analysis of classifica-tion methodsaddition of multiple features
NETL
NSTL
万方数据
