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
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f14081630

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 learning algorithms to estimate forest ecological function levels based on multi-source data, including Sentinel-2 optical remote sensing images and digital elevation model (DEM) and forest resource planning and design survey data. The experimental results showed that Random Forest (RF) was the optimal model, with overall accuracy of 0.82, recall of 0.66, and F1 of 0.62, followed by CatBoost (overall accuracy = 0.82, recall = 0.62, F1 = 0.58) and LightGBM (overall accuracy = 0.76, recall = 0.61, F1 = 0.58). Except for the indicators from remote sensing images and DEM data, the five ground survey indicators of forest origin (QI_YUAN), tree age group (LING_ZU), forest category (LIN_ZHONG), dominant species (YOU_SHI_SZ), and tree age (NL) were used in the modeling and prediction. Compared to the traditional methods, the proposed algorithm has lower cost and stronger timeliness.