Predicting forest stand attributes using the integration of airborne laser scanning and Worldview-3 data in a mixed forest in Turkey

The aim of this study is to examine the capability of the combined LiDAR/WorldView-3 data in estimating the plot-level stand attributes (stem number-N, mean diameter-D, mean height-H, basal area-BA and volume-V) in a complex forest located in the northwest of Turkey. Total 135 plots were measured to determine the forest attributes. Prediction models were developed at three levels which are: i) the general level for all stands (including all plots), ii) forest type level (coniferous forest, broad-leaved forest), and iii) tree species level (Black pine stands, Maritime pine stands, Oak stands, Mixed stands). Multiple Linear Regression (MLR) and Random Forest (RF) modelling approaches were tested to predict stand attributes. The MLR regression modelling showed that the stand attributes were estimated with R 2 ranging from 0.71 (N and V in Mixed) to 0.94 (H in Maritime pine) at tree species level, from 0.73 (BA in Broadleaved) to 0.95 (H in Conifer) at forest types level and from 0.77 (V) to 0.89 (H) at general level. The RF modelling indicated that the stand attributes were estimated with R 2 ranging from 0.69 (V in Mixed and Oak) to 0.94 (H in Maritime pine) at tree species level, from 0.72 (N in Broadleaved) to 0.95 (H in Conifer) at forest types level and from 0.81 (N and V) to 0.88 (D) at general level. The mean height had the highest prediction accuracy for almost all levels in both approaches. However, the stem number and basal area were generally estimated with the lower accuracies. The homogeneous coniferous stands provided the higher estimation accuracy than the broadleaved stands. Our results showed that the modelling approaches used here provide different performance for predicting different stand attributes. While the MLR approach performed better in estimating the stand attributes at the tree species level, the RF approach towards the general level provided higher accuracy estimation. In conclusion, the combination of aerial laser scanning and high resolution satellite data has high potential for predicting stand attributes in complex forest ecosystems.