Assessment of Forest Ecological Function Levels Based on Multi-Source Data and Machine Learning

• Published in: Forests Vol. 14; no. 8; p. 1630
• Main Authors: Fang, Ning, Yao, Linyan, Wu, Dasheng, Zheng, Xinyu, Luo, Shimei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Algorithms; Biomass; Digital Elevation Models; Digital imaging; Dominant species; Ecological function; Ecosystem services; forest ecological function index; forest ecological function level; Forest resources; Forestry; Indicators; Land area; Learning algorithms; Machine learning; Model accuracy; multi-source data; Recall; Remote sensing; Research methodology; Surveys; Weighting methods; China; Zhejiang China; France; Paris France

Forest ecological function is one of the key indicators reflecting the quality of forest resources. The traditional weighting method to assess forest ecological function is based on a large amount of ground survey data; it is accurate but costly and time-consuming. This study utilized three machine...