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

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 R2 reaches 0.89, a value that compares favorably with those in other studies.

Regional Forest Structure Evaluation Model Based on Remote Sensing and Field Survey Data

“…Moreover, the CatBoost algorithm can autonomously determine the importance of variables, thereby augmenting the accuracy and usability of the model. Researchers that combine field survey data with remote sensing data have demonstrated significant advancements using the CatBoost algorithm to estimate forest biomass or assess forest quality [63,64]. Moreover, we posit that the CatBoost algorithm is a promising methodology for establishing forest structure assessment models using remote sensing data, meriting deeper exploration and broader implementation.…”

Forest Resource Quality and Human Activity Intensity Change and Spatial Autocorrelation Analysis in Yulin City, China

“…Based on the regional characteristics of forest resources and the needs of sustainable forest management, Wu et al (2010) established a forest resource quality evaluation index system and a BP neural network for forest resource quality assessment and evaluated the forest resources in Hubei Province, China [9]. By referring to and analyzing forest quality indicators from domestic and foreign experts and institutions, Cao et al (2023) constructed a concept and model of forest growth potential and calculated the forest growth potential based on the data of 110,000 forest resource sub-areas in Lin'an and Landsat 8 remote sensing data [10]. They also discussed a forest resource quality improvement program in the subregion to provide guidance for accurately improving forest resource quality and forest management [10].…”

“…By referring to and analyzing forest quality indicators from domestic and foreign experts and institutions, Cao et al (2023) constructed a concept and model of forest growth potential and calculated the forest growth potential based on the data of 110,000 forest resource sub-areas in Lin'an and Landsat 8 remote sensing data [10]. They also discussed a forest resource quality improvement program in the subregion to provide guidance for accurately improving forest resource quality and forest management [10]. In summary, forest resource quality assessment is of great significance for understanding the status of forest resources and improving forest resource quality and forest management.…”