Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France

Riparian forests are valuable environments delivering multiples ecological services. Because they face both natural and anthropogenic constraints, riparian forests need to be accurately mapped in terms of genera/species diversity. Previous studies have shown that the Airborne Laser Scanner (ALS) data have the potential to classify trees in different contexts. However, an assessment of important features and classification results for broadleaved deciduous riparian forests mapping using ALS remains to be achieved. The objective of this study was to estimate which features derived from ALS data were important for describing trees genera from a riparian deciduous forest, and provide results of classifications using two Machine Learning algorithms. The procedure was applied to 191 trees distributed in eight genera located along the S?lune river in Normandy, northern France. ALS data from two surveys, in the summer and winter, were used. From these data, trees crowns were extracted and global morphology and internal structure features were computed from the 3D points clouds. Five datasets were established, containing for each one an increasing number of genera. This was implemented in order to assess the level of discrimination between trees genera. The most discriminant features were selected using a stepwise Quadratic Discriminant Analysis (sQDA) and Random Forest, allowing the number of features to be reduced from 144 to 3?9, depending on the datasets. The sQDA-selected features highlighted the fact that, with an increasing number of genera in the datasets, internal structure became more discriminant. The selected features were used as variables for classification using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Additionally, Random Forest classifications were conducted using all features computed, without selection. The best classification performances showed that using the sQDA-selected features with SVM produced accuracy ranging from 83.15% when using three genera (Oak, Alder and Poplar). A similar result was obtained using RF and all features available for classification. The latter also achieved the best classification performances when using seven and eight genera. The results highlight that ML algorithms are suitable methods to map riparian trees.