Assessing Forest Quality through Forest Growth Potential, an Index Based on Improved CatBoost Machine Learning

Lianjun Cao, Xiaobing He, Sheng Chen and Luming Fang ()
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Lianjun Cao: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Xiaobing He: Baishanzu Scientific Research Monitoring Center, Qianjiangyuan-Baishanzu National Park, Lishui 323000, China
Sheng Chen: Zhejiang Forest Resources Monitoring Center, Hangzhou 311300, China
Luming Fang: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China

Sustainability, 2023, vol. 15, issue 11, 1-18

Abstract: Human activities have always depended on nature, and forests are an important part of this; the determination and improvement of forest quality is therefore highly significant. Currently, domestic and foreign research on forest quality focuses on the current states of forests. We propose a new research direction based on the future states. By referencing and analyzing the forest quality standards of domestic and foreign experts and institutions, the concept and model for calculating forest growth potential were constructed. Forest growth potential is a new forest quality indicator. Based on the data of 110,000 subcompartments of forest resources from the Lin’an and Landsat8 satellites’ remote sensing data, the unit volume was predicted using three machine-learning algorithms: random gradient descent SGD, the integrated machine learning algorithm CatBoost, and deep learning CNN. The CatBoost algorithm model was improved based on Optuna; then the improved CatBoost algorithm was selected through evaluation indicators for the prediction of forest volume and finally incorporated into the calculation model for forest growth-potential value. The forest growth-potential value was calculated, and an accurate forest quality improvement scheme based on the subcompartments is preliminarily discussed. The successful calculation of forest growth potential values has a certain reference significance, providing guidance for accurately improving forest quality and forest management. The improved CatBoost calculation model is effective in the prediction of forest growth potential, and the determination coefficient R 2 reaches 0.89, a value that compares favorably with those in other studies.

Keywords: forest growth potential; precise improvement of forest quality; CatBoost; SGD; CNN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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