Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data

This study presents an approach for semi-automated classification of tree species in different types of forests using first and second generation ADS40 and RC30 images from two study areas located in the Swiss Alps. In a first step, high-resolution canopy height models (CHMs) were generated from the ADS40 stereo-images. In a second step, multi-resolution image segmentation was applied. Based on image segments seven different tree species for study area 1 and four for study area 2 were classified by multinomial regression models using the geometric and spectral variables derived from the ADS40 and RC30 images. To deal with the large number of explanatory variables and to find redundant variables, model diagnostics and step-wise variable selection were evaluated. Classifications were ten-fold cross-validated for 517 trees that had been visited in field surveys and detected in the ADS40 images. The overall accuracies vary between 0.76 and 0.83 and Cohen's kappa values were between 0.70 and 0.73. Lower accuracies (kappa < 0.5) were obtained for small samples of species such as non-dominant tree species or less vital trees with similar spectral properties. The usage of NIR bands as explanatory variables from RC30 or from the second generation of ADS40 was found to substantially improve the classification results of the dominant tree species. The present study shows the potential and limits of classifying the most frequent tree species in different types of forests, and discusses possible applications in the Swiss National Forest Inventory.