Tree species classification in the Southern Alps with very high geometrical resolution multispectral and hyperspectral data

In this paper we analyze the problem of tree species classification in the Southern Alps by using high geometrical resolution airborne hyperspectral data. In addition, we study the effects of downscaling the spectral resolution through the use of very high geometrical resolution satellite images. The analysis is carried out on data acquired over a mountain area in the Southern Alps. This area is characterized by eleven tree species, both coniferous and broadleaved, distributed in topographically complex site. For each data source a specific processing chain was developed and a Support Vector Machine classifier was used. The experimental results made it clear that airborne hyperspectral data are effective for tree species classification in complex mountain areas (kappa accuracy of about 0.78). The spectral downscaling to very high resolution satellite multispectral images allows one to keep the spatial detail of the analysis but reducing significantly the level of accuracy in class discrimination (acceptable results were obtained only for macro-classes of species, for which the kappa accuracy was 0.70).

1. INTRODUCTION

The accurate characterization of tree species distribution in forest areas is an important task as it can drive the exploitation and management policies of forests. In fact, the knowledge of the tree species present in an area can be used for many purposes: forest inventories, analysis of trends and distribution of specific species, definition of policies of fire management, etc.