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

Human activities have always depended on nature, and forests are an important part of this; the determination improvement forest quality is therefore highly significant. Currently, domestic foreign research focuses current states forests. We propose a new direction based future states. By referencing analyzing standards experts institutions, concept model for calculating growth potential were constructed. Forest indicator. Based data 110,000 subcompartments resources from Lin’an Landsat8 satellites’ remote sensing data, unit volume was predicted using three machine-learning algorithms: random gradient descent SGD, integrated machine learning algorithm CatBoost, deep CNN. The CatBoost improved Optuna; then selected through evaluation indicators prediction finally incorporated into calculation growth-potential value. value calculated, accurate scheme preliminarily discussed. successful values has certain reference significance, providing guidance accurately improving management. effective in potential, coefficient R2 reaches 0.89, that compares favorably with those other studies.