Methodsological Study on the Detection of the Variations of Forest Resources Based on C5.0 Algorithm-A Case of Culai Forest in Shandong

With double-temporal TM and ETM+ remote sensing data,the information of the variation of forest resources of Culai Mountain in Shandong Province,China was explored.Decision tree classification based on C5.0 algorithm to forest change detection was applied.Three different detection methods were compared:1) to classify single-temporal data by C5.0 respectively,and extract change information after comparing classification results;2) to create C5.0 train rules through double-temporal raw data,then generate change detection map;3) in addition to double-temporal remote sensing data,neighborhood correlation analysis images are also added as one of the data sources of C5.0,and generate change detection map of variation.The experimental result shows that decision tree classification based on C5.0 algorithm could detect variation information effectively,and after adding neighborhood correlation analysis images the classification accuracy of change detection was improved.