Erik Næsset

Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data

Abstract The mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 144 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 1000-ha study area, and the size of each plot was 200 m 2 . On the average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 82–95%, 74–93%, 39–78%, 50–68%, 69–89%, and 80–93% of the variability in ground-truth mean height, dominant height, mean diameter, stem number, basal area, and volume, respectively. A proposed practical two-stage procedure for prediction of corresponding characteristics of entire forest stands was tested. Sixty-one stands within the study area, with an average size of 1.6 ha each, were divided into 200 m 2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data utilizing the estimated regression equations. Average values for each stand was computed. Most stand level predictions were unbiased ( P >.05). Standard deviations of the differences between predicted and ground-truth values of mean height, dominant height, mean diameter, stem number, basal area, and volume were 0.61–1.17 m, 0.70–1.33 m, 1.37–1.61 cm, 16.9–22.2% (128–400 ha −1 ), 8.6–11.7% (2.33–2.54 m 2 ha −1 ), and 11.4–14.2% (18.3–31.9 m 3 ha −1 ), respectively.

Determination of mean tree height of forest stands using airborne laser scanner data

Abstract The mean tree height of forest stands is a crucial stand characteristic in forest planning. Currently, the mean tree height is determined by field measurements or by photogrammetric measurements utilizing aerial photographs. In this study, mean tree height of 36 test stands is derived from tree canopy heights measured by means of an airborne laser scanner. On the average the laser recorded 505–1070 canopy heights per stand. First, the laser mean height is computed as the arithmetic mean of the canopy heights within each stand. The laser mean height underestimates the ground truth mean height by 4.1–5.5 m. Second, a weighted mean of the laser canopy heights is computed. The individual height values are used as weights. The weighted mean height underestimates the true height by 2.1–3.6 m. Finally, the laser mean height is computed as the arithmetic mean of the largest laser values within square grid cells with cell sizes of 15–30 m. The bias of the laser estimates is in the range −0.4 m to 1.9 m. The standard deviation for differences between the laser mean heights and the ground truth mean height is 1.1–1.6 m.

Estimating timber volume of forest stands using airborne laser scanner data

Abstract The stand volumes of 36 Norway spruce (Pieea abies Karst.) and Scots pine (Pinus sylvestris L.) stands were derived from various tree canopy height metrics and canopy cover density measured by means of an airborne laser scanner. On average, the laser transmitted 1350–1910 pulses per stand and recorded 505–1070 canopy hits with corresponding estimates of canopy height. Ground truth stand volume was regressed against mean stand height, the mean height of all laser pulses within a stand, and canopy cover density as determined from the laser data. The coefficients of determination were in the range between 0.456 and 0.887. The coefficients of variation ranged from 17.2% to 43.3%.

Practical large-scale forest stand inventory using a small-footprint airborne scanning laser

Mean tree height, dominant height, mean diameter, stem number, basal area and timber volume of 116 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 6500 ha study area, and the size of each plot was 232.9 m2. Regressions for coniferous forest explained 60–97% of the variability in ground reference values of the six studied characteristics. A proposed practical two-phase procedure for prediction of corresponding characteristics of entire forest stands was tested. Fifty-seven test plots within the study area with a size of approximately 3740 m2 each were divided into 232.9 m2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data using the estimated regression equations. Average values for each test plot were computed and compared with ground-based estimates measured over the entire plot. The bias and standard deviations of the differences between predicted and ground reference values (in parentheses) of mean height, dominant height, mean diameter, stem number, basal area and volume were −0.58 to −0.85 m (0.64–1.01 m), −0.60 to −0.99 m (0.67–0.84 m), 0.15–0.74 cm (1.33–2.42 cm), 34–108 ha−1 (97–466 ha−1), 0.43–2.51 m2 ha−1 (1.83–3.94 m2 ha−1) and 5.9–16.1 m3 ha−1 (15.1–35.1 m3 ha−1), respectively.

Laser scanning of forest resources: the nordic experience

This article reviews the research and application of airborne laser scanning for forest inventory in Finland, Norway and Sweden. The first experiments with scanning lasers for forest inventory were conducted in 1991 using the FLASH system, a full-waveform experimental laser developed by the Swedish Defence Research Institute. In Finland at the same time, the HUTSCAT profiling radar provided experiences that inspired the following laser scanning research. Since 1995, data from commercially operated time-of-flight scanning lasers (e.g. TopEye, Optech ALTM and TopoSys) have been used. Especially in Norway, the main objective has been to develop methods that are directly suited for practical forest inventory at the stand level. Mean tree height, stand volume and basal area have been the most important forest mensurational parameters of interest. Laser data have been related to field training plot measurements using regression techniques, and these relationships have been used to predict corresponding properties in all forest stands in an area. Experiences from Finland, Norway and Sweden show that retrieval of stem volume and mean tree height on a stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. Laser scanning is, therefore, now beginning to be used operationally in large-area forest inventories. In Finland and Sweden, research has also been done into the identification of single trees and estimation of single-tree properties, such as tree position, tree height, crown width, stem diameter and tree species. In coniferous stands, up to 90% of the trees represented by stem volume have been correctly identified from canopy height models, and the tree height has been estimated with a root mean square error of around 0.6 m. It is significantly more difficult to identify suppressed trees than dominant trees. Spruce and pine have been discriminated on a single-tree level with 95% accuracy. The application of densely sampled laser scanner data to change detection, such as growth and cutting, has also been demonstrated.

Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve

Tree height, the height from the ground surface to the tree crown, and the crown length as a proportion of tree height of individual trees were derived from various canopy height metrics measured by a small-footprint airborne laser scanner flown over a boreal forest reserve. The average spacing on the ground of the laser pulses ranged from 0.66 to 1.29 m. Ground-truth values were regressed against laser-derived canopy height metrics. The regressions explained 75%, 53%, and 51% of the variability in ground-truth tree height, height to the crown, and relative crown length, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 3.15 m (17.6%), 2.19 m (39.1%), and 10.48% (14.9% of ground-truth mean), respectively. On 10 plots with size 50 m2 in the boreal forest reserve and on 27 plots with size 200 m2 in a managed spruce forest, mean tree height, average height from the ground surface to the crown, and average relative crown length were regressed against laser canopy height metrics. The coefficients of determination (R2) ranged from .47 to .91. Cross-validation revealed a precision of 1.49 m (7.6%), 1.24–1.52 m (20.9–23.3%), and 6.32–7.11% (8.8–10.9% of ground-truth mean) for mean tree height, average height to the crown, and average relative crown length, respectively. At least, mean tree height can be determined more accurately from laser data than by current methods.

Estimating tree heights and number of stems in young forest stands using airborne laser scanner data

The mean heights of dominant trees and the stem numbers of 39 plots of 200 m2 each were derived from various canopy height metrics and canopy density measured by means of a small-footprint airborne laser scanner over young forest stands with tree heights .05) and a standard deviation of the differences between predicted and ground-truth mean height of 0.56 m (8.4%).

Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project

This research reports the major results from an evaluation of the first Nordic operational stand-based forest inventory using airborne laser scanner data. Laser data from a forest area of 250 km2 were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m2 field training plots distributed systematically throughout the forest area. Test plots with an approximate size of 0.1–0.4 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.36–1.37 m (1.9–7.6%) for mean height, 0.70–1.55 m (3.0–7.6%) for dominant height, 2.38–4.88 m2 ha−1 (7.8–14.2%) for basal area and 13.9–45.9 m3 ha−1 (6.5–13.4%) for stand volume. No serious bias was detected.

An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppa (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.

Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest

In this study, we present a new method for single tree segmentation and characterization from a canopy surface model (CSM), and its corresponding point cloud, based on airborne laser scanning. The method comprises new algorithms for controlling the shape of crown segments, and for residual adjustment of the canopy surface model (CSM). We present a new criterion that measures the success of locating trees, and demonstrate how this criterion can be used for optimizing the degree of CSM smoothing. From the adjusted CSM segments, we derived tree height and crown diameter, and based on all first laser pulse measurements within the segments we derived crown-base height. The method was applied and validated in a Norway spruce dominated forest reserve having a heterogeneous structure. The number of trees automatically detected varied with social status of the trees, from 93 percent of the dominant trees to 19 percent of the suppressed trees. The RMSE values for tree height, crown diameter, and crown-base height were around 1.2 m, 1.1 m, and 3.5 m, respectively. The method overestimated crown diameter (0.8 m) and crown base height (3.0 m).