Kenneth Olofsson

Comparison of three individual tree crown detection methods

Three image processing methods for single tree crown detection in high spatial resolution aerial images are presented and compared using the same image material and reference data. The first method uses templates to find the tree crowns. The other two methods uses region growing. One of them is supported by fuzzy rules while the other uses an image produced by Brownian motion. All three methods detect around 80%, or more, of the visible sunlit trees in two pine Pinus Sylvestris L.) and two spruce stands Picea abies Karst.) in a boreal forest. For all methods, large tree crowns are easier to detect than small ones.

Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm

Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and the RANSAC algorithm and (2) assess the influence of distance to the scanner on the measurement accuracy. Tree detection and stem diameter estimates were validated for 16 circular plots with 20 m radius. The three dominating tree species were Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). The proportion of detected trees decreased as the distance to the scanner increased and followed the trend of decreasing visible area. Within 10 m from the scanner, the proportion of detected trees was 87% on average for the plots and the diameter at breast height was estimated with a relative root-mean-square-error (RMSE) of 14%. The most accurate diameter measurements were obtained for pine, which had a RMSE of 7% for all the full 20 m radius plots. The RANSAC algorithm reduced noise and made it possible to obtain reliable estimates.

Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods

Individual tree crown segmentation from airborne laser scanning (ALS) data often fails to detect all trees depending on the forest structure. This paper presents a new method to produce tree lists consistent with unbiased estimates at area level. First, a tree list with height and diameter at breast height (DBH) was estimated from individual tree crown segmentation. Second, estimates at plot level were used to create a target distribution by using a k-nearest neighbour (k-NN) approach. The number of trees per field plot was rescaled with the estimated stem volume for the field plot. Finally, the initial tree list was calibrated using the estimated target distribution. The calibration improved the estimates of the distributions of tree height (error index (EI) from 109 to 96) and DBH (EI from 99 to 93) in the tree list. Thus, the new method could be used to estimate tree lists that are consistent with unbiased estimates from regression models at field plot level.

Forest stand delineation from lidar point-clouds using local maxima of the crown height model and region merging of the corresponding Voronoi cells

In this study, a novel automatic forest stand segmentation method based on Voronoi cells and Airborne Laser Scanner data was developed and validated using a systematic grid of field plots. The automatic method produce results comparable to manual stand delineation.

Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

ABSTRACT This study had the aim of investigating the utility of image-based point cloud data for estimation of vertical canopy cover (VCC). An accurate measure of VCC based on photogrammetric matching of aerial images would aid in vegetation mapping, especially in areas where aerial imagery is acquired regularly. The test area is located in southern Sweden and was divided into four vegetation types with sparse to dense tree cover: unmanaged coniferous forest; pasture areas with deciduous tree cover; wetland; and managed coniferous forest. Aerial imagery with a ground sample distance of 0.24 m was photogrammetrically matched to produce dense image-based point cloud data. Two different image matching software solutions were used and compared: MATCH-T DSM by Trimble and SURE by nFrames. The image-based point clouds were normalized using a digital terrain model derived from airborne laser scanner (ALS) data. The canopy cover metric vegetation ratio was derived from the image-based point clouds, as well as from raster-based canopy height models (CHMs) derived from the point clouds. Regression analysis was applied with vegetation ratio derived from near nadir ALS data as the dependent variable and metrics derived from image-based point cloud data as the independent variables. Among the different vegetation types, vegetation ratio derived from the image-based point cloud data generated by using MATCH-T resulted in relative root mean square errors (rRMSE) of VCC ranging from 6.1% to 29.3%. Vegetation ratio based on point clouds from SURE resulted in rRMSEs ranging from 7.3% to 37.9%. Use of the vegetation ratio based on CHMs generated from the image-based point clouds resulted in similar, yet slightly higher values of rRMSE.

Estimating tree stem density and diameter distribution in single-scan terrestrial laser measurements of field plots: a simulation study

ABSTRACT The single-scan setup of terrestrial laser scanning of a forest field plot has advantages compared to the multi-scan setup: the speed of operation and that there is no need of a co-registration of the different scans. However in a single-scan setup some of the trees are shaded by others and therefore not detected in the scan. A field inventory solution must take this fact into account. This simulation study shows how different plot sizes and tree stand densities influence the stem visibility giving nonlinear effects especially for large trees and high stem numbers. These effects can be counteracted by using an edge or center stem point detection criteria when analyzing the results or by weighting the detected trees by their visibility. It is shown that the stem density and diameter distribution can be estimated from the visible areas of the plot in case the stem positions are Poisson distributed.

Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning

Abstract The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees – thus validating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance – achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality.