Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

3rd International Conference on Advances in Energy, Environment and Chemical Engineering

能源学;生态环境科学;化学工业

Luo, Qiu^1 ; Xin, Wu^2 ; Qiming, Xiong^2

School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, China^1

Central South University of Forestry and Technology, Changsha, Hunan, China^2

关键词: Accuracy of classifications;    Adaptive selection;    Multi-Sources;    Recognition accuracy;    Remote sensing analysis;    Tree similarities;    Vegetation remote sensing;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/69/1/012162/pdf
DOI  :  10.1088/1755-1315/69/1/012162

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87% - 98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80% - 96% in this area. Therefore, the validity of the proposed method can be verified.