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Water tends to flow and accumulate in response to topographical characteristics of local area and gravitational potential energy. Remote sensing data like LiDAR (Light detecting and ranging) or satellite data can be used to identify local depressions where wet areas may occur. The aim of this study was to evaluate methods that can be used to identify wet areas, to determine correlation between topography of the area and forest regeneration and to prepare proposals for forest management that could be usable in Latvia. Study area includes fertile forest land on wet mineral soils and drained mineral soils with planted spruce (Picea abies) and available LiDAR data. Map examples have been made to demonstrate methodology which allows to identify depressions with potentially hindered run-off. Fill sinks algorithm has shown best results in identifying wet areas and correlation with wet areas that were detected in field studies is 62%. TWI index is not suitable for this study because of relatively flat area. Result of this study reveals that wet areas have significant effect on tree species. In depressions, despite the fact that there has been planted spruce, main species are birch (Betula pendula) and black alder (Alnus glutinosa). Wet areas have significant effect on tree height.

IDENTIFICATION OF WET AREAS IN FOREST BY USING LIDAR BASED DEM