Vegetation Information Extraction for Restoration of Sandy Land in Northwest Sichuan Based on Unmanned Aerial Vehicles and Machine Learning
[Objective]This study aims to extract vegetation information(herbs and shrubs)from UAV images,and estimate vegetation coverage,finally reflecting vegetation growth and abundance in the field of ecological restoration.[Method]Four types of surface objects including water,shrubs,herbs and sand were selected,and four machine learning algorithms,including deep learning,Mahalanobis dis-tance,maximum likelihood method and minimum distance method,were used for precision comparison.The algorithm with the highest accuracy is selected as the research method.[Result]The overall accu-racy of the four methods were 95.47％,95.14％,93.30％and 71.98％,and kappa coefficient were 0.92,0.91,0.88 and 0.57,respectively.[Conclusion]The optimal method of the four algorithms was the deep learning method.The water body and sandy land are 0.09,0.14,0.04 and 0.32 km2,respectively.This method can provide data support and scientific basis for monitoring,research and management evaluation of alpine restoration sandy land in northwest Sichuan.
high-resolution unmanned aerial vehicle imagessandy vegetation information extractionplant coveragemachine learning
NETL
NSTL
万方数据
维普
