Research on Forest Resource Change Detection Based on Decision Tree Algorithm

Abstract

Forest resource change detection has always been an important task of forest resource administrative departments. It is of great significance to know the situation of resource changes in time and put an end to illegal logging and ultra-intensive logging. Forest resource status and its growth and decline are directly related to the formulation of forestry planning and forestry production policies. As an important method in data mining, decision tree data mining has been applied to all walks of life. Decision tree is an example-based inductive algorithm, which focuses on reasoning out the classification rules of decision tree representation from a group of unordered and irregular cases. In this paper, the change of forest resources is detected based on the decision tree algorithm. By comparing the change detection results of different feature vectors, conventional change vector analysis and difference method, better detection methods and results of forest resources change are obtained. Decision tree algorithm is also constantly groping for forest resource change detection, adjusting the operation system of the technology, analyzing the data more accurately, improving its accuracy and processing efficiency, and constantly optimizing it, so that its application field is wider.