Mango is a commercial crop on Hainan Island,China,that is cultivated to develop the tropical rural economy.The development of accurate and up-to-date maps of the spatial distribution of mango plantations is necessary ...Mango is a commercial crop on Hainan Island,China,that is cultivated to develop the tropical rural economy.The development of accurate and up-to-date maps of the spatial distribution of mango plantations is necessary for agricultural monitoring and decision management by the local government.Pixel-based and object-oriented image analysis methods for mapping mango plantations were compared using two machine learning algorithms(support vector machine(SVM)and Random Forest(RF))based on Chinese high-resolution Gaofen-1(GF-1)imagery in parts of Hainan Island.To assess the importance of different features on classification accuracy,a combined layer of four original bands,32 gray-level co-occurrence(GLCM)texture indices,and 10 vegetation indices were used as input features.Then five different sets of variables(5,10,20,and 30 input variables and all 46 variables)were classified with the two machine learning algorithms at object-based level.Results of the feature optimization suggested that homogeneity and variance were very important variables for distinguishing mango plantations patches.The object-based classifiers could significantly improve overall accuracy between 2–7%when compared to pixel-based classifiers.When there were 5 and 10 input variables,SVM showed higher classification accuracy than RF,and when the input variables exceeded 20,RF showed better performances.After the accuracy achieved saturation points,there were only slightly classification accuracy improvements along with the numbers of feature increases for both of SVM and RF classifiers.The results indicated that GF-1 imagery can be successfully applied to mango plantation mapping in tropical regions,which would provide a useful framework for accurate tropical agriculture land management.展开更多
基金This research was funded by the Opening Foundation Program of Land Use Key Laboratory of the Ministry of Natural Resources of China,the Hainan Provincial Key Laboratory of Practical Research on Tropical Crops Information Technology,China(RDZWKFJJ2019001)the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences(1630072020006,1630072017004 and 1630072019001)+2 种基金the Natural Science Foundation of Hainan,China(619MS100)the National Natural Science Foundation of China(31601211)the Demonstration and Pilot Projects for Comprehensive Rural Reform,China(XXSNZG19-02).
文摘Mango is a commercial crop on Hainan Island,China,that is cultivated to develop the tropical rural economy.The development of accurate and up-to-date maps of the spatial distribution of mango plantations is necessary for agricultural monitoring and decision management by the local government.Pixel-based and object-oriented image analysis methods for mapping mango plantations were compared using two machine learning algorithms(support vector machine(SVM)and Random Forest(RF))based on Chinese high-resolution Gaofen-1(GF-1)imagery in parts of Hainan Island.To assess the importance of different features on classification accuracy,a combined layer of four original bands,32 gray-level co-occurrence(GLCM)texture indices,and 10 vegetation indices were used as input features.Then five different sets of variables(5,10,20,and 30 input variables and all 46 variables)were classified with the two machine learning algorithms at object-based level.Results of the feature optimization suggested that homogeneity and variance were very important variables for distinguishing mango plantations patches.The object-based classifiers could significantly improve overall accuracy between 2–7%when compared to pixel-based classifiers.When there were 5 and 10 input variables,SVM showed higher classification accuracy than RF,and when the input variables exceeded 20,RF showed better performances.After the accuracy achieved saturation points,there were only slightly classification accuracy improvements along with the numbers of feature increases for both of SVM and RF classifiers.The results indicated that GF-1 imagery can be successfully applied to mango plantation mapping in tropical regions,which would provide a useful framework for accurate tropical agriculture land management.
