Simulation in the woods: From remote sensing based data acquisition and processing to various simulation applications

Abstract

This paper focuses on joint work towards the development of simulation applications in the forest sector. They are based on advanced “semantic” world modeling techniques which use remote sensing data and processing algorithms to derive tree species classification maps, as well as forest stand attributes and single tree databases over large areas. The resulting databases are the basis for a variety of different simulation applications in an integrated system approach. Forest growth simulations aim to predict the appearance of the forest in the next decades. Forest machine simulators allow for an efficient development of forest machines and their control algorithms, as well as for cost-effective driver training. Harvesting cost simulations calculate the harvesting costs long before the lumbermen start to work. Decision support systems enable wood owners and the wood industry to compare different treatment scenarios based on simulations and thus to comprehensively assess ecological and economical chances and consequences.