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FOREST SURVEY BASED ON REMOTE SENSING

A. Bücken, J. Roßmann
Wednesday 19 December 2018 by Libadmin2018

ABSTRACT

An up to date forest inventory is the key precondition for a sustainable forest usage. Especially for owners of smaller forests, the inventory is a major cost factor, as they do not generate huge revenues from their forestry units but still have to pay the forest surveyor. One result of this situation is, that many owners do not use the timber in their forests and the timber volume, which is available for the industry, is significantly lower than it should be. The solution is a cost-efficient inventory, which can be performed automatically based on already available standard data sets. In this paper we present a novel approach for a remote sensing based inventory, which – for the first time – can also address the stand quality and the age of the trees. This results in a significantly increased precision compared to former approaches. A forest surveyor collects information on the distribution of species for a stand and in addition for each species the basal area, the dominant or medium height and the age. Based on the height and the age he assigns a yield class as a quality index. This can be done with height tables, which can be found in the yield tables, a tabular description of typical tree growth for several species. Based on the yield class, age and basal area, he can then calculate a yield factor and with this he can determine the timber volume for the forestry unit as well as the increment in timber volume per decade. Until now, a remote sensing based inventory was able to address the height data and estimated the combination of age and yield factor based on typical stands. This can lead to a significant discrepancy of up to 40 percent in the timber volume when the estimated yield class differs too much from the correct one. While this is not a problem for owners of multiple larger units, where the average result is still within the inaccuracy of a terrestrial census, it is critical for small owners. We therefore addressed this issue and developed an approach to derivate the yield class within small bounds and therefore raise the precision for the individual unit.

Keywords: Forestry, Timber Volume, Remote Sensing


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