Juha Hyyppä

A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners

In the boreal forest zone and in many forest areas, there exist gaps between the forest crowns. For example, in Finland, more than 30% of the first pulse data of laser scanning reflect directly from the ground without any interaction with the canopy. By increasing the number of pulses, it is possible to have samples from each individual tree and also from the gaps between the trees. Basically, this means that several laser pulses can be recorded per m/sup 2/. This allows detailed investigation of forest areas and the creation of a three-dimensional (3D) tree height model. Tree height model can be calculated from the digital terrain and crown models both obtained with the laser scanner data. By analyzing the 3D tree height model by using image vision methods, e.g., segmentation, it is possible to locate individual trees, estimate individual tree heights, crown area, and, by using that data, to derive the stem diameter, number of stems, basal area, and stem volume. The advantage of the method is the capability to measure directly physical dimensions from the trees and use that information to calculate the needed stand attributes. This paper demonstrates for the first time that it is possible to accurately estimate standwise forest attributes, especially stem volume (biomass), using high-pulse-rate laser scanners to provide data, from which individual trees can be detected and characteristics of trees such as height, location, and crown dimensions can be determined. That information can be applied to provide estimates for larger areas (stands). Using the new method, the following standard errors were demonstrated for mean height, basal area and stem volume: 1.8 m (9.9%), 2.0 m/sup 2//ha (10.2%), and 18.5 m/sup 3//ha (10.5%), 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.

Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests

Experiences from Nordic countries and Canada have shown that the retrieval of the stem volume and mean tree height of a tree or at stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. This paper reviews the methods of small‐footprint airborne laser scanning for extracting forest inventory data, mainly in the boreal forest zone. The methods are divided into the following categories: extraction of terrain and canopy height model; feature extraction approaches (canopy height distribution and individual‐tree‐based techniques, techniques based on the synergetic use of aerial images and lidar, and other new approaches); tree species classification and forest growth using laser scanner; and the use of intensity and waveform data in forest information extraction. Despite this, the focus is on methods, some review of quality obtained, especially in the boreal forest area, is included. Several recommendations for future research are given to foster the methodology development.

Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes.

Recent advances in developing new airborne instruments and space-borne missions and in SAR technology, especially in interferometry and coherence estimation, have roused questions: can such new SAR data be utilized in operational forest inventory? What is the accuracy of different satellite data for forest inventory? This paper verifies the explanatory power and information contents of several remote sensing data sources on the retrieval of stem volume, basal area, and mean height, utilizing the following data: Landsat TM, Spot PAN and XS, ERS-1/2 PRI and SLC (coherence estimation), airborne data from imaging spectrometer AISA, radar-derived forest canopy profiles (obtained with HUTSCAT), and aerial photographs. Ground truth data included three different sets ranging from conventional forest inventory data to intensive field checking where one man-day was spent for assessing one stand. Multivariate and neural network methods were applied in data analysis. The results suggested that (1) radar-derived stand profiles obtained with 100 m spacing was the most accurate data source in this comparison and was of equivalent accuracy with conventional forest inventory for mean height and stem volume estimation, (2) aerial photographs (scale 1 : 20,000) gave comparable results with the imaging spectrometer AISA, (3) the satellite images used for the estimation in the decreasing explanation power were Spot XS, Spot PAN, Landsat TM, ERS SAR coherence, JERS SAR intensity images (PRI); and ERS SAR intensity images (PRI). It appears that optical images still include more information for forest inventory than radar images, (4) from all satellite radar methods, the coherence technique seemed to be superior to other methods.

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.

Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data

Accurate road environment information is needed in applications such as road maintenance and virtual 3D city modelling. Vehicle-based laser scanning (VLS) can produce dense point clouds from large areas efficiently from which the road and its environment can be modelled in detail. Pole-like objects such as traffic signs, lamp posts and tree trunks are an important part of road environments. An automatic method was developed for the extraction of pole-like objects from VLS data. The method was able to find 77.7% of the poles which were found by a manual investigation of the data. Correctness of the detection was 81.0%.

Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning

The demand for detailed ground reference data in quantitative forest inventories is growing rapidly, e.g., to improve the calibration of the developed models of airborne-laser-scanning-based inventories. The application of terrestrial laser scanning (TLS) in the forest has shown great potential for improving the accuracy and efficiency of field data collection. This paper presents a fully automatic stem-mapping algorithm using single-scan TLS data for collecting individual tree information from forest plots. In this method, the stem points are identified by the spatial distribution properties of the laser points, the stem model is built up of a series of cylinders, and the location of the stem is estimated by the model. The experiment was performed on nine plots with 10-m radius. The stem-location maps measured in the field by traditional methods were used as the ground truth. The overall stem-mapping accuracy was 73%. The result shows that, in a relatively dense managed forest, the majority of stems can be located by the automatic algorithm. The proposed method is a general solution for stem locating where particular plot knowledge and data format are not required.

Retrieval Algorithms for Road Surface Modelling Using Laser-Based Mobile Mapping

Automated processing of the data provided by a laser-based mobile mapping system will be a necessity due to the huge amount of data produced. In the future, vehicle-based laser scanning, here called mobile mapping, should see considerable use for road environment modelling. Since the geometry of the scanning and point density is different from airborne laser scanning, new algorithms are needed for information extraction. In this paper, we propose automatic methods for classifying the road marking and kerbstone points and modelling the road surface as a triangulated irregular network. On the basis of experimental tests, the mean classification accuracies obtained using automatic method for lines, zebra crossings and kerbstones were 80.6%, 92.3% and 79.7%, respectively.

Advances in Forest Inventory Using Airborne Laser Scanning

We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the last pulse results in a drop in elevation, due to its better penetration between the trees. This drop in elevation can be used for separating trees. In a test carried out in Evo, Southern Finland, we used 292 forests plots consisting of more than 5,500 trees and airborne laser scanning (ALS) data comprised of 12.7 emitted laser pulses per m2. With last pulse data, an improvement of 6% for individual tree detection was obtained when compared to using first pulse data. The improvement increased with an increasing number of stems per plot and with decreasing diameter breast height (DBH). The results confirm that there is also substantial information for tree detection in last pulse data. The second improvement is based on the use of individual tree-based features in addition to the statistical point height metrics in area-based prediction of forest variables. The commonly-used ALS point height metrics and individual tree-based features were fused into the non-parametric estimation of forest variables. By using only four individual tree-based features, stem volume estimation improved when compared to the use of statistical point height metrics. For DBH estimation, the point height metrics and individual tree-based features complemented each other. Predictions were validated at plot level.

Study of surface brightness from backscattered laser intensity: calibration of laser data

Systematic laboratory measurements of laser backscatter intensity are presented for brightness calibration targets, and a calibration scheme for airborne laser scanner intensity data is proposed. Thus far, the use of these data has been partly hampered by the variability of the intensity with time, and no test fields have been available for airborne reflectance calibration. Portable brightness targets (tarps), with nominal reflectances from 5% to 70%, were manufactured, and, based on these measurements, found suitable for lidar reflectance standards. Furthermore, the variability of the recorded intensity from the tarps as a function of incidence angle was low. The measurements also provide new information on the surface albedo dependence of backscattering effects: as the surface brightness increases from 5% to 70%, the hotspot brightness peak amplitudes increase by 20% to 30%, and their apparent widths reduce to a half, which implies that hotspots could be used as an albedo discriminator.