Using Machine Learning to Assess Site Suitability for Afforestation with Particular Species

Chen Chen,Wu Wu,Qi Qi

doi:10.3390/f10090739

Abstract

Judging and predicting tree suitability is of great significance in the cultivation and management of forests. Background and Objectives: Due to the diversity of tree species for afforestation in China and the lack of experts or the limitations of expert knowledge, the site rules of tree species in some regions are lacking or incomplete, so that a small number of tree suitability empirical site rules are difficult to adapt to the afforestation expert system’s diverse needs. Research Highlights: This paper explores an intelligent method to automatically extract rules for selecting favorable site conditions (tree suitability site rules) from a large amount of data to solve the problem of knowledge acquisition, updating and maintenance of suitable forest site rules in the expert system. Materials and Methods: Based on the method of site quality evaluation and the theory of the decision tree in knowledge discovery and machine learning, the dominant species of Chinese fir and Masson pine in the forest resources subcompartment data (FRSD) of Jinping County, Guizhou Province were taken as examples to select the important site factors affecting the forest quality and based on the site quality of potential productivity. Assessment methodology was proposed to determine the afforestation of a stand site by nonlinear quantile regression, the decision tree was constructed from the ID3, C5.0 and CART algorithms. Results: Finally, the best-performing CART algorithm was selected to construct the model, and the extractor of the afforestation rules was constructed. After validating the rules for selecting favorable site conditions of Chinese fir and Masson pine, the production representation method was used to construct the relationship model of the knowledge base. Conclusions: Intelligent extraction of suitable tree rules for afforestation design in an expert system was realized, which provided the theoretical basis and technical support for afforestation land planning and design.

Highlights

Tree suitability involves adapting the afforestation characteristics of a tree species to the site conditions to give full play to the productive potential of forests and achieve a higher level of productivity of afforestation tree species under the current technical and economic conditions of the site
The knowledge of site rules was represented by the production rule method in this paper
To solve the problems of incomplete expert knowledge and difficult acquisition, this paper proposed to determine forest site suitability by quantile regression, constructed a decision tree of the ID3, C5.0 and CART algorithms, and selected the best performing CART algorithm to construct the model

Summary

Introduction

Tree suitability involves adapting the afforestation characteristics of a tree species to the site conditions to give full play to the productive potential of forests and achieve a higher level of productivity of afforestation tree species under the current technical and economic conditions of the site. The disadvantage of the site index is that it is difficult to directly explain the productivity level of site (i.e., tree suitability performance) through the SI value This is because the planting density and the relationship between tree height and diameter at breast height (1.3 m)(DBH) depend on the tree species, the relationship between SI value and yield of different tree species is different [1,2]. Because the average volume growth is affected by the site conditions and by the stand density and management level [1,2,22], it is necessary to consider complex conditions (different regions, site conditions, tree species and management measures) when using average volume growth as an evaluation index

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Discussion

Conclusion