Forest canopy cover estimation with machine learning using GEDI and Landsat data in the Western Marmara Region, Türkiye

Abstract Forests play a crucial role in carbon dioxide absorption, biodiversity preservation, and climate regulation. Timely and precise maps detailing tree cover attributes are vital tools for the fields of environmental research and natural resource management. This study addresses the challenge of accurately estimating forest canopy cover by integrating Global Ecosystem Dynamics Investigation (GEDI) and Landsat data in the Eastern Marmara Region of Türkiye. Despite the advancements in remote sensing technologies such as light detection and ranging (LiDAR) and optical sensors, and the importance of forest canopy cover in assessing forest health and carbon stocks, there is limited research on utilizing spaceborne GEDI Level 2B data for forest canopy cover mapping. Six different machine learning methods were employed, namely Classification and Regression Trees (CART), Categorical Boosting (CTB), Light Gradient Boosting Machines (LGBM), Multilayer Perceptron (MLP), Random Forest (RF), and Extreme Gradient Boosting (XGB), to generate forest canopy cover maps using Landsat 8 and 9 satellite images with a variety of vegetation indices and texture features. Model performances were evaluated using metrics such as R 2 , Root Mean Square Error (RMSE), and Median Absolute Error (MdAE), with statistical significance assessed via Friedman and Wilcoxon signed-rank tests. The results of the tests indicated that the XGB (R 2 = 0.5570, RMSE = 0.1603, MdAE = 0.0885) and RF (R 2 = 0.5497, RMSE = 0.1617, MdAE = 0.0896) algorithms, which were trained with GEDI Level 2B data, provided greater accuracy in forest canopy cover estimation compared to the other algorithms. This study offers insight into the prediction performance of GEDI Level 2B spaceborne LiDAR data in conjunction with XGB and RF algorithms for forest canopy cover estimation and underscores the significance of integrating advanced remote sensing data for forest monitoring.