Semiautomatic classification of tree species by means of multitemporal airborne digital sensor data ADS40

Temporally frequent, cost-efficient and precise forest information requirements for national forest inventories, monitoring or protection tasks have grown over time and will continue to do so in the future. New perspectives are given by the airborne digital sensor ADS40, which provides entire image strips with high geometric, radiometric and temporal resolution (every three years for entire Switzerland). This study presents an approach for semi-automated tree species classification in different types of forests using multi-temporal ADS40-SH40 and ADS40-SH52 images from May and July 2007 and August 2008 to support tasks of the Swiss National Forest Inventory. Based on image segments seven different tree species were classified by combined logistic regression models using spectral variables derived from each of the three different ADS40 images. Additional classification was established combining the May and July 2007 imagery. Explanatory variables were derived from each image data set using a step-wise variable selection. Classifications were five-fold cross-validated for 230 trees that had been visited in field surveys and detected in the ADS40 images. The 7 tree species were therefore classified up to four times providing its spectral variability during the vegetation period. The overall accuracies vary between 0.67 and 0.8 and Cohen's kappa values between 0.6 and 0.69 whereas the classification based on the May 2007 images performed best. Independent from the sensors and acquisition date of the images lowest accuracies were obtained for Acer sp.This study reveals the potential and limits of the ADS40 data to classify tree species and underscores the advantage of a multi-temporal classification of deciduous tree species with spectral similarities. * Corresponding author

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