Asa Persson

Identifying species of individual trees using airborne laser scanner

Abstract Individual trees can be detected using high-density airborne laser scanner data. Also, variables characterizing the detected trees such as tree height, crown area, and crown base height can be measured. The Scandinavian boreal forest mainly consists of Norway spruce ( Picea abies L. Karst.), Scots pine ( Pinus sylvestris L.), and deciduous trees. It is possible to separate coniferous from deciduous trees using near-infrared images, but pine and spruce give similar spectral signals. Airborne laser scanning, measuring structure and shape of tree crowns could be used for discriminating between spruce and pine. The aim of this study was to test classification of Scots pine versus Norway spruce on an individual tree level using features extracted from airborne laser scanning data. Field measurements were used for training and validation of the classification. The position of all trees on 12 rectangular plots (50×20 m 2 ) were measured in field and tree species was recorded. The dominating species (>80%) was Norway spruce for six of the plots and Scots pine for six plots. The field-measured trees were automatically linked to the laser-measured trees. The laser-detected trees on each plot were classified into species classes using all laser-detected trees on the other plots as training data. The portion correctly classified trees on all plots was 95%. Crown base height estimations of individual trees were also evaluated ( r =0.84). The classification results in this study demonstrate the ability to discriminate between pine and spruce using laser data. This method could be applied in an operational context. In the first step, a segmentation of individual tree crowns is performed using laser data. In the second step, tree species classification is performed based on the segments. Methods could be developed in the future that combine laser data with digital near-infrared photographs for classification with the three classes: Norway spruce, Scots pine, and deciduous trees.

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.

Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images

The objectives of this study were to identify useful predictive factors for tree species identification of individual trees and to compare classifications based on a combination of LiDAR data and multi‐spectral images with classification by the use of each individual data source. Crown segments derived from LiDAR data were mapped to multi‐spectral images for extraction of spectral data within individual tree crowns. Several features, related to height distribution of laser returns in the canopy, canopy shape, proportion of different types of laser returns, and intensity of laser returns, were derived from LiDAR data. Data from a test site in southern Sweden were used (lat. 58°30′ N, long. 13°40′ E). The forest consisted of Norway spruce (Picea abies), Scots pine (Pinus sylvestris), and deciduous trees. Classification into these three tree species groups was validated for 1711 trees that had been detected in LiDAR data within 14 field plots (sizes of 20×50 m2 or 80×80 m2). The LiDAR data were acquired by the TopEye MkII system (50 LiDAR measurements per m2) and the multi‐spectral images were taken by the Zeiss/Intergraph Digital Mapping Camera. The overall classification accuracy was 96% when both data sources were combined.

Three-dimensional environment models from airborne laser radar data

Detailed 3D environment models for visualization and computer based analyses are important in many defence and homeland security applications, e.g. crisis management, mission planning and rehearsal, damage assessment, etc. The high resolution data from airborne laser radar systems for 3D sensing provide an excellent source of data for obtaining the information needed for many of these models. To utilise the 3D data provided by the laser radar systems however, efficient methods for data processing and environment model construction needs to be developed. In this paper we will present some results on the development of laser data processing methods, including methods for data classification, bare earth extraction, 3D-reconstruction of buildings, and identification of single trees and estimation of their position, height, canopy size and species. We will also show how the results can be used for the construction of detailed 3D environment models for military modelling and simulation applications. The methods use data from discrete return airborne laser radar systems and digital cameras.

Segmentation and classification of airborne laser scanner data for ground and building detection

In this paper, a number of techniques for segmentation and classification of airborne laser scanner data are presented. First, a method for ground estimation is described, that is based on region growing starting from a set of ground seed points. In order to prevent misclassification of buildings and vegetation as ground, a number of non-ground regions are first extracted, in which seed points should be discarded. Then, a decision-level fusion approach for building detection is proposed, in which the outputs of different classifiers are combined in order to improve the final classification results. Finally, a technique for building reconstruction is briefly outlined. In addition to being a tool for creating 3D building models, it also serves as a final step in the building classification process since it excludes regions not belonging to any roof segment in the final building model.

Characterizing targets and backgrounds for 3D laser radars

Exciting development is taking place in 3 D sensing laser radars. Scanning systems are well established for mapping from airborne and ground sensors. 3 D sensing focal plane arrays (FPAs) enable a full range and intensity image can be captured in one laser shot. Gated viewing systems also produces 3 D target information. Many applications for 3 D laser radars are found in robotics, rapid terrain visualization, augmented vision, reconnaissance and target recognition, weapon guidance including aim point selection and others. The net centric warfare will demand high resolution geo-data for a common description of the environment. At FOI we have a measurement program to collect data relevant for 3 D laser radars using airborne and tripod mounted equipment for data collection. Data collection spans from single pixel waveform collection (1 D) over 2 D using range gated imaging to full 3 D imaging using scanning systems. This paper will describe 3 D laser data from different campaigns with emphasis on range distribution and reflections properties for targets and background during different seasonal conditions. Example of the use of the data for system modeling, performance prediction and algorithm development will be given. Different metrics to characterize the data set will also be discussed.

On analysis and visualization of full-waveform airborne laser scanner data

The ongoing technical developments on airborne laser scanner systems, with shorter pulses, increased operation altitudes, focal plane array detectors, full-waveform digitization and recoding, etc. provide new opportunities for the expansion and growth of military as well as civilian applications. However, for the continuing development of systems and applications one crucial issue is the research and development of new and efficient laser data processing methods for analysis and visualization. In this paper we will present some recent developments on visualization and analysis of full-waveform data. We will discuss visualization of waveform data by inserting the waveform samples in a 3D volume consisting of small 3D cells referred to as voxels. We will also present an approach for extracting additional 3D point data from the waveforms. The long term goal of this research is to develop methods for automated extraction of natural as well as man-made objects. The aim is to support the construction of high-fidelity 3D virtual environment models and detection and identification of man-made objects.

Performance of 3D laser radar through vegetation and camouflage

One of the more exciting capabilities foreseen for future 3-D imaging laser radars is to see through vegetation and camouflage nettings. We have used ground based and airborne scanning laser radars to collect data of various types of terrain and vegetation. On some occasions reference targets were used to collect data on reflectivity and to evaluate penetration. The data contains reflectivity and range distributions and were collected at 1.5 and 1.06 μm wavelength with range accuracies in the 1-10 cm range. The seasonal variations for different types of vegetation have been studied. A preliminary evaluation of part of the data set was recently presented at another SPIE conference. Since then the data have been analyzed in more detail with emphasis on testing algorithms and future system performance by simulation of different sensors and scenarios. Evaluation methods will be discussed and some examples of data sets will be presented.

Methods for recognition of natural and man-made objects using laser radar data

Over the years imaging laser radar systems have been developed for both military and civilian (topographic) applications. Among the applications, 3D data is used for environment modeling and object reconstruction and recognition. The data processing methods are mainly developed separately for military or topographic applications, seldom both application areas are in mind. In this paper, an overview of methods from both areas is presented. First, some of the work on ground surface estimation and classification of natural objects, for example trees, is described. Once natural objects have been detected and classified, we review some of the extensive work on reconstruction and recognition of man-made objects. Primarily we address the reconstruction of buildings and recognition of vehicles. Further, some methods for evaluation of measurement systems and algorithms are described. Models of some types of laser radar systems are reviewed, based on both physical and statistical approaches, for analysis and evaluation of measurement systems and algorithms. The combination of methods for reconstruction of natural and man-made objects is also discussed. By combining methods originating from civilian and military applications, we believe that the tools to analyze a whole scene become available. In this paper we show examples where methods from both application fields are used to analyze a scene.

Characterizing laser radar snow reflection for the wavelengths 0.9 and 1.5 μ

This paper will describe measurements of snow reflection using laser radar. There seems to be a rather limited number of publications on snow reflection related to laser radar, which is why we decided to investigate a little more details of snow reflection including that from different kinds of snow as well as the angular reflection properties. We will discuss reflectance information obtained by two commercial scanning laser radars using the wavelengths 0.9 μm and 1.5 μm. Data will mainly be presented at the eye safe wavelength 1.5 μm but some measurements were also performed for the wavelength 0.9 μm. We have measured snow reflection during a part of a winter season which gave us opportunities to investigate different types of snow and different meteorological conditions. The reflection values tend to decrease during the first couple of hours after a snowfall. The snow structure seems to be more important for the reflection than the snow age. In general the snow reflection at 1.5 μm is rather low and the reflectivity values can vary between 0.5 and 10 % for oblique incidence depending on snow structure which in turn depends on age, air temperature, humidity etc. The snow reflectivity at the 0.9 μm laser wavelength is much higher, more than 50 % for fresh snow. Images of snow covered scenes will be shown together with reflection data including BRDFs.