基于机器学习的天然混交林单木生长模型

混交林已经成为应对气候变化的一种重要的适应性森林经营模式，持续得到全球关注。混交林的生长预估一直是森林生长收获预估领域的难点，对混交林的种间关系和生长规律并不完全清楚，并面临气候变化的新挑战。人工智能的发展为混交林生长建模提供了新的思路。本项目探索“如何基于机器学习方法来建立混交林的生长模型，认识混交林的生长规律，并模拟气候变化和森林经营的影响？”这一重要亟待解决的科学问题。以东北温带针阔混交林为对象，将固定样地定期调查数据、树轮数据和直径微生长数据相结合，基于人工神经网络和随机森林等5种机器学习方法，建立包含竞争、气候、结构和混交效应的单木生长模型，并与传统的统计方法进行比较；分析混交林内共存树种间的相互关系和生长规律，模拟间伐和未来气候变化及其交互作用对针阔混交林生长的影响。预期结果将发展混交林生长模拟的新方法，弥补混交林生长规律的知识，为天然针阔混交林的适应性经营提供依据。

Mixed-species forest has become an important management practice adaptive to climate change, and attained great attention all over the world. Growth modelling for mixed-species stands is still a difficult task in the field of forest growth and yield prediction, however. There is knowledge gap in the growth and inter-specific interaction of mixed-species stands. Climate change is a new challenge to the model. Artificial intelligence development gives a new way to forest growth and yield modelling. Therefore, the project aims to answer the question how to employ machine learning methods to describe the growth and simulate the effects of climate change, forest management and their interaction on the growth of mixed-species forests. Individual tree growth models will be developed based on artificial neural network (ANN) and other 5 assemble machine learning methods (random forest, support vector engine, bagging regression and mboost regression) and traditional statistical models (nonlinear mixed-effects model, generalized additive model). The data come from permanent sample plots，tree ring and dendrometer data. The model will include the effects of competition, climate, stand structure and mixture. Statistical and machine learning based models will be evaluated and compared. The species-specific growth and inter-specific interaction of mixed-species stands will be analyzed from the model. The impact of future climate change and thinning and their interaction will be explored from the simulation with different scenarios. It is expected to provide novel method for mixed-species forest growth modelling and fill the knowledge gap on the growth and adaptive management of mixed conifer-broad-leaved forests.