3D Modeling Lidar Surveying

Lidar Survey Supports Tree Segmentation Research

images of Lidar Survey Key to Improving Tree Segmentation
Lidar Survey Key to Improving Tree Segmentation

Accurate knowledge of the actual tree population is becoming increasingly important in forestry. The accumulation of extreme weather events and dry periods caused by climate change have a serious impact on both managed and semi-natural forests. The current stand of a forest is traditionally determined in the forest inventory by measuring selected, representative groups of trees and subsequent extrapolation to the total area. By using a lidar survey, it is possible to record a forest over its entire area and thus avoid the errors caused by extrapolation. This ultimately leads to a much more accurate picture of the actual condition.

The first step of such an inventory is the detection of the individual trees in the point cloud. Ideally, various tree parameters can then be derived from the points representing a tree: Height, crown diameter, base height of the crown, breast height diameter, etc.

At the Department of Geoinformatics at Munich University of Applied Sciences, a group of researchers led by Dr. Peter Krzystek has been working for years on efficient methods for single tree detection from area-wide ALS data.

In their recent publication, the researchers now present the latest development in the field of tree segmentation, “Single tree detection has been a major research topic when it comes to support of collecting automatic field inventory using lidar. All previous methods show under-segmentation and over-segmentation effects because the associated control parameters have a limited scope. This paper describes a novel integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection. The key idea is to replace the static stopping criterion, which is usually defined by trial and error or by a sensitivity analysis, here with a query for whether a stem position has been provided by the stem detection in the remaining cluster to be partitioned. The stem detection automatically detects tree stems by identifying vertical lines based on a hierarchical classification procedure.”

For the full article on the use of a lidar survey CLICK HERE.

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