Tree stem diameter estimation from terrestrial point clouds

• Forest owners, governments, and environmental organizations demand forest information for planning of forest operations, estimation of value, and for environmental monitoring. This information is collected using airborne and satellite remote sensing combined with field inventory of sample plots. Stem diameter is measured with calipers, which is labor-intensive. Terrestrial sensors could make the inventory faster, and more samples could be taken. Sensors mounted on forest harvesters could produce maps of the trees left after forest operations, or collect data for an operator support system. The first article describes a photogrammetric method using a multi-camera rig for estimation of stem diameter and position on field plots. Problematic light conditions reduced the usable amount of field plots. On adequate field plots, 76% of the trees were detected and positioned, and on 40% of the trees the diameters could be estimated. In the second article, the results from a mobile laser scanning project was improved by treating the data line-wise, and by using the intensity of the laser points as a quality value. The RMSE of the stem diameters was reduced from 24% to 14%, but the bias increased slightly from -1.9% to 2.3%. The edge points on the stems were identified as an error source since they were not found along the expected circle. The third article investigates this edge point problem by simulation of laser scanner/tree combinations. A relationship between the diameter error and the footprint size relative to the stem diameter was found. Commonly used mobile laser scanners were concluded to give a relative bias of 10% or more when estimating diameters using circle fit methods. In the fourth article, a panorama image of the intensities of a laser scanner point cloud was used to detect trees, with adequate results. The overall conclusions are that point clouds from the various sensors are useful for estimation of tree diameter and positions, but they have sensor-dependent properties that can introduce errors. These properties, and the precision requirement should be considered when the data acquisition is planned and the sensor is selected.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Environmental Engineering -- Remote Sensing (hsv//eng)

LANTBRUKSVETENSKAPER  -- Lantbruksvetenskap, skogsbruk och fiske -- Skogsvetenskap (hsv//swe)

AGRICULTURAL SCIENCES  -- Agriculture, Forestry and Fisheries -- Forest Science (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

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