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The paper presents a simple, inexpensive, and effective method allowing for frequent classification of the forest type coniferous, deciduous, and mixed using medium and low resolution remote sensing images. The proposed method is based on the set of vegetation indices such as NDVI, LAI, FAPAR, and LAIxCab calculated from MODIS and MERIS satellite data. The method uses seasonal changes of the above-mentioned vegetation indices within annual cycle. The main idea was to collect and carefully analyse seasonal changes in vegetation indices in a given ecosystem type proven by a Corine Land Cover, 2006 database, and to compare them afterwards with those of a particular forest under study. Each type of a forest ecosystem has its own specific dynamics of development, thus enabling recognition of the type by comparing temporal changes of the proposed measures based on vegetation indices. Temporal measures of changes were created for selected reference stands by the ratios of particular indices determined in July and April, which are the middle and the beginning of a vegetation season in Poland, respectively. The analysed vegetation indices were additionally provided with chosen statistical measures. The statistical analyses were carried out for Poland’s main national parks which represent the natural stands of temperate climate.

isrn forestry

Simple Method of Forest Type Inventory by Joining Low Resolution Remote Sensing of Vegetation Indices with Spatial Information from the Corine Land Cover Database