Evaluating Lorenz entropy for tropical forest discrimination using GEDI and supervised machine learning approach

• Lorenz-entropy (LE) index improves tropical forest classification with GEDI data. • LE index quantifies vertical complexity across tropical forest ecosystems. • Integration of entropy index enhances machine learning classification metrics. • Ensemble classifiers outperform others with entropy-based forest metrics. Analyzing the vertical structural complexity of tropical forests is essential for understanding their ecological functions and biodiversity. Given this significance, an indicator that quantifies entropy, representing heterogeneity and disorder in structural complexity, plays a significant role in forest ecological studies. This study explored the potential of the Lorenz-entropy (LE) index as an innovative metric for classifying tropical forest types. Using spaceborne LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI) mission from April 2019 to March 2023, we integrated the LE index with supervised machine learning algorithms to evaluate its effectiveness in distinguishing vertical structural complexity across the three tropical forest ecosystems. In addition to the LE index, forest structural variables such as Above Ground Biomass Density (AGBD), Plant Area Index (PAI), and Relative Height 98 (RH98) were included. The results revealed that incorporating the LE index into remote sensing-based forest monitoring frameworks can significantly improve the classification of tropical forest types. Paired t-tests confirmed statistically significant improvements (p < 0.05) in the classification metrics with substantial effect sizes measured using Cohen’s d. Moreover, ensemble methods, mainly Random Forest (RF) and Gradient Boosting classifiers, exhibited the highest accuracy, with RF showing 90 ± 2 % overall accuracy and XGBoost 90 ± 2.5 % upon incorporating the LE index. These findings highlight the utility of the LE index as a metric of vertical structural complexity and underscore its value in improving tropical forest discrimination using GEDI data. Future research should explore integrating additional remote sensing data to refine the application of the LE index for forest classification.