Abstract
Changes to stems caused by natural forces and timber harvesting constitute an essential input for many forestry-related applications and ecological studies, especially forestry inventories based on the use of permanent sample plots. Conventional field measurement is widely acknowledged as being time-consuming and labor-intensive. More automated and efficient alternatives or supportive methods are needed. Terrestrial laser scanning (TLS) has been demonstrated to be a promising method in forestry field inventories. Nevertheless, the applicability of TLS in recording changes in the structure of forest plots has not been studied in detail. This paper presents a fully automated method for detecting changes in forest structure over time using bi-temporal TLS data. The developed method was tested on five densely populated forest plots including 137 trees and 50 harvested trees in point clouds. The present study demonstrated that 90 percent of tree stem changes could be automatically located from single-scan TLS data. These changes accounted for 92 percent of the changed basal area. The results indicate that the processing of TLS data collected at different times to detect tree stem changes can be fully automated.
Highlights
The stem of a tree holds most of its volume, biomass and economic value [1]
E.g., permanent sample plots, changes can be detected from the tree stumps using manual interpretation and by referring to stem-location maps recorded at different times by conducting field measurements
This paper presents a fully automated algorithm using bi-temporal terrestrial laser scanning data to detect harvested stems in a typical boreal forest environment
Summary
The stem of a tree holds most of its volume, biomass and economic value [1]. Knowledge of the changes occurring in the stem status constitutes an essential input into studies concerning forest management and harvesting operations [2]. Fallen and harvested trees significantly change the forest structure The detection of these changes can be made using field inventories and remote sensing (RS) techniques. E.g., permanent sample plots, changes can be detected from the tree stumps using manual interpretation and by referring to stem-location maps recorded at different times by conducting field measurements. The advantage of applying TLS data to forest inventories is the improvement in the accuracy and efficiency of field measurements, and the capability of locating trees on the plot and deriving stem curve data [20]. This paper presents a fully automated method for the detection of changes in forest structure over time using bi-temporal TLS data. The developed method was tested by analyzing harvested stems between two TLS data acquisitions
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