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Norbert Pfeifer - One of the best experts on this subject based on the ideXlab platform.
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Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types
Remote Sensing, 2014Co-Authors: András Zlinszky, Balázs Deák, Anke Schroiff, Adam Kania, Werner Mücke, Ágnes Vári, Balázs Székely, Norbert PfeiferAbstract:There is increasing demand for reliable, high-resolution vegetation maps covering large areas. Airborne Laser Scanning data is available for large areas with high resolution and supports automatic processing, therefore, it is well suited for habitat mapping. Lowland hay meadows are widespread habitat types in European grasslands, and also have one of the highest species richness. The objective of this study was to test the applicability of Airborne Laser Scanning for vegetation mapping of different grasslands, including the Natura 2000 habitat type lowland hay meadows. Full waveform leaf-on and leaf-off point clouds were collected from a Natura 2000 site in Sopron, Hungary, covering several grasslands. The LIDAR data were processed to a set of rasters representing point attributes including reflectance, echo width, vegetation height, canopy openness, and surface roughness measures, and these were fused to a multi-band pseudo-image. Random forest machine learning was used for classifying this dataset. Habitat type, dominant plant species and other features of interest were noted in a set of 140 field plots. Two sets of categories were used: five classes focusing on meadow identification and the location of lowland hay meadows, and 10 classes, including eight different grassland vegetation categories. For five classes, an overall accuracy of 75% was reached, for 10 classes, this was 68%. The method delivers unprecedented fine resolution vegetation maps for management and ecological research. We conclude that high-resolution full-waveform LIDAR data can be used to detect grassland vegetation classes relevant for Natura 2000.
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opals a framework for Airborne Laser Scanning data analysis
Computers Environment and Urban Systems, 2014Co-Authors: Norbert Pfeifer, Gottfried Mandlburger, Johannes Otepka, Wilfried KarelAbstract:Abstract A framework for Orientation and Processing of Airborne Laser Scanning point clouds, OPALS, is presented. It is designed to provide tools for all steps starting from full waveform decomposition, sensor calibration, quality control, and terrain model derivation, to vegetation and building modeling. The design rationales are discussed. The structure of the software framework enables the automatic and simultaneous building of command line executables, Python modules, and C++ classes from a single algorithm-centric repository. It makes extensive use of (industry-) standards as well as cross-platform libraries. The framework provides data handling, logging, and error handling. Random, high-performance run-time access to the originally acquired point cloud is provided by the OPALS data manager, allowing storage of billions of 3D-points and their additional attributes. As an example geo-referencing of Laser Scanning strips is presented.
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radiometric calibration of multi wavelength Airborne Laser Scanning data
ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2012Co-Authors: Christian Briese, Wolfgang Wagner, Martin Pfennigbauer, Andreas Ullrich, Hubert Lehner, Norbert PfeiferAbstract:Abstract. Airborne Laser Scanning (ALS) is a widely used technique for the sampling of the earth's surface. Nowadays a wide range of ALS sensor systems with different technical specifications can be found. One parameter is the Laser wavelength which leads to a sensitivity for the wavelength dependent backscatter characteristic of sensed surfaces. Current ALS sensors usually record next to the geometric information additional information on the recorded signal strength of each echo. In order to utilize this information for the study of the backscatter characteristic of the sensed surface, radiometric calibration is essential. This paper focuses on the radiometric calibration of multi-wavelength ALS data and is based on previous work on the topic of radiometric calibration of monochromatic (single-wavelength) ALS data. After a short introduction the theory and whole workflow for calibrating ALS data radiometrically based on in-situ reference surfaces is presented. Furthermore, it is demonstrated that this approach for the monochromatic calibration can be used for each channel of multi-wavelength ALS data. The resulting active multi-channel radiometric image does not have any shadows and from a geometric viewpoint the position of the objects on top of the terrain surface is not altered (the result is a multi-channel true orthophoto). Within this paper the approach is demonstrated by three different single-wavelength ALS data acquisition campaigns (532nm, 1064nm and 1550nm) covering the area of the city Horn (Austria). The results and practical issues are discussed.
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categorizing wetland vegetation by Airborne Laser Scanning on lake balaton and kis balaton hungary
Remote Sensing, 2012Co-Authors: András Zlinszky, Werner Mücke, Christian Briese, Hubert Lehner, Norbert PfeiferAbstract:Outlining patches dominated by different plants in wetland vegetation provides information on species succession, microhabitat patterns, wetland health and ecosystem services. Aerial photogrammetry and hyperspectral imaging are the usual data acquisition methods but the application of Airborne Laser Scanning (ALS) as a standalone tool also holds promises for this field since it can be used to quantify 3-dimensional vegetation structure. Lake Balaton is a large shallow lake in western Hungary with shore wetlands that have been in decline since the 1970s. In August 2010, an ALS survey of the shores of Lake Balaton was completed with 1 pt/m2 discrete echo recording. The resulting ALS dataset was processed to several output rasters describing vegetation and terrain properties, creating a sufficient number of independent variables for each raster cell to allow for basic multivariate classification. An expert-generated decision tree algorithm was applied to outline wetland areas, and within these, patches dominated by Typha sp. Carex sp., and Phragmites australis. Reed health was mapped into four categories: healthy, stressed, ruderal and die-back. The output map was tested against a set of 775 geo-tagged ground photographs and had a user’s accuracy of > 97% for detecting non-wetland features (trees, artificial surfaces and low density Scirpus stands), > 72% for dominant genus detection and > 80% for most reed health categories (with 62% for one category). Overall classification accuracy was 82.5%, Cohen’s Kappa 0.80, which is similar to some hyperspectral or multispectral-ALS fusion studies. Compared to hyperspectral imaging, the processing chain of ALS can be automated in a similar way but relies directly on differences in vegetation structure and actively sensed reflectance and is thus probably more robust. The data acquisition parameters are similar to the national surveys of several European countries, suggesting that these existing datasets could be used for vegetation mapping and monitoring.
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INVESTIGATING ADJUSTMENT OF Airborne Laser Scanning STRIPS WITHOUT USAGE OF GNSS/IMU TRAJECTORY DATA
2009Co-Authors: Camillo Ressl, Gottfried Mandlburger, Norbert PfeiferAbstract:Airborne Laser Scanning (ALS) requires GNSS (Global Navigation Satellite System; e.g. GPS) and an IMU (Inertial Measurement Unit) for determining the dynamically changing orientation of the Scanning system. Because of small but existing instabilities of the involved parts - especially the mounting calibration - a strip adjustment is necessary in most cases. In order to realize this adjustment in a rigorous way the GNSS/IMU-trajectory data is required. In some projects this data is not available to the user (any more). Derived from the rigorous model, this article presents a model for strip adjustment without GNSS/IMU-trajectory data using five parameters per strip: one 3D shift, one roll angle, and one affine yaw parameter. In an example with real data consiting of 61 strips this model was successfully applied leading to an obvious improvement of the relative accuracy from (59.3/23.4/4.5) [cm] to (7.1/7.2/2.2) (defined as RMS values in (X/Y/Z) of the differences of corresponding points derived by least squares matching in the overlapping strips). This example also clearly demonstrates the importance of the affine yaw parameter.
Matti Maltamo - One of the best experts on this subject based on the ideXlab platform.
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bayesian approach to tree detection based on Airborne Laser Scanning data
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Timo Lahivaara, Timo Tokola, Jari Vauhkonen, Aku Seppanen, J P Kaipio, Lauri Korhonen, Matti MaltamoAbstract:In this paper, we consider a computational method for detecting trees on the basis of Airborne Laser Scanning (ALS) data. In the approach, locations, heights, and crown shapes of trees are tracked automatically by fitting multiple 3-D crown height models to ALS data of a field plot. The estimates are computed with an iterative reconstruction method based on Bayesian inversion paradigm. The formulation allows for utilizing prior information on tree shapes in the estimation. Here, the prior models are written based on field measurement data and allometric models for tree shapes. The feasibility of the approach is tested with ALS and field data from a managed boreal forest. The algorithm found 70.2% of the trees in the area, which is a clear improvement compared to a usual 2.5D crown delineation approach (53.1% of the trees detected).
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canonical correlation analysis for interpreting Airborne Laser Scanning metrics along the lorenz curve of tree size inequality
Baltic Forestry, 2014Co-Authors: Ruben Valbuena, Petteri Packalen, Timo Tokola, Matti MaltamoAbstract:Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequality
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forestry applications of Airborne Laser Scanning concepts and case studies
2014Co-Authors: Matti Maltamo, Erik Næsset, Jari VauhkonenAbstract:1. Introduction to forest applications of Airborne Laser Scanning Jari Vauhkonen et al.- PART I - Methodological issues.- 2. Laser pulse interaction with forest canopy - geometric and radiometric issues Andreas Roncat et al.- 3. Full-waveform Airborne Laser Scanning systems and their possibilities in forest applications Markus Hollaus et al.- 4. Integrating Airborne Laser Scanning with data from global navigation satellite systems and optical sensors Ruben Valbuena.- 5. Segmentation of forest to tree objects Barbara Koch et al.- 6. The semi-individual tree crown approach Johannes Breidenbach, Rasmus Astrup.- 7. Tree species recognition based on Airborne Laser Scanning and complementary data sources Jari Vauhkonen et al.- 8. Estimation of biomass components by Airborne Laser Scanning Sorin C. Popescu, Marius Hauglin.- 9. Predicting tree diameter distributions Matti Maltamo, Terje Gobakken.- 10. A model-based approach for the recovery of forest attributes using Airborne Laser Scanning data Lauri Mehtatalo et al.- PART II - Forest inventory applications.- 11. Area-based inventory in Norway - from innovation to an operational reality Erik Naesset.- 12. Species specific management inventory in Finland Matti Maltamo, Petteri Packalen.- 13. Inventory of forest plantations Jari Vauhkonen et al.- 14. Using Airborne Laser Scanning data to support forest sample surveys Ronald E. McRoberts et al.- 15. Modeling and estimating change Ronald E. McRoberts et al.- 16. Valuation of Airborne Laser Scanning based forest information Annika Kangas et al.- PART III - Ecological applications.- 17. Assessing habitats and organism-habitat relationships by Airborne Laser Scanning Ross A. Hill et al.- 18. Assessing biodiversity by Airborne Laser Scanning Jorg Muller, Kerri Vierling.- 19. Assessing dead wood by Airborne Laser Scanning Matti Maltamo et al.- 20. Estimation of canopy cover, gap fraction and leaf area index with Airborne Laser Scanning Lauri Korhonen, Felix Morsdorf.- 21. Canopy gap detection and analysis with Airborne Laser Scanning Benoit St-Onge et al.- 22. Applications of Airborne Laser Scanning in forest fuel assessment and fire prevention John Gajardo et al.- Index.
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introduction to forestry applications of Airborne Laser Scanning
2014Co-Authors: Jari Vauhkonen, Matti Maltamo, Ronald E Mcroberts, Erik NæssetAbstract:Airborne Laser Scanning (ALS) has emerged as one of the most promising remote sensing technologies to provide data for research and operational applications in a wide range of disciplines related to management of forest ecosystems. This chapter starts with a brief historical overview of the early forest-related research on Airborne Light Detection and Ranging which was first mentioned in the literature in the mid-1960s. The early applications of ALS in the mid-1990s are also reviewed. The two fundamental approaches to use of ALS in forestry applications are presented – the area-based approach and the single-tree approach. Many of the remaining chapters rest upon this basic description of these two approaches. Finally, a brief introduction to the broad range of forestry applications of ALS is given and references are provided to individual chapters that treat the different topics in more depth. Most chapters include detailed reviews of previous research and the state-of-the-art in the various topic areas. Thus, this book provides a unique collection of in-depth reviews and overviews of the research and application of ALS in a broad range of forest-related disciplines.
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patterns of covariance between Airborne Laser Scanning metrics and lorenz curve descriptors of tree size inequality
Canadian Journal of Remote Sensing, 2013Co-Authors: Ruben Valbuena, Matti Maltamo, Petteri Packalen, Susana Martinfernandez, Cristina Pascual, G J NabuursAbstract:The Lorenz curve, as a descriptor of tree size inequality within a stand, has been suggested as a reliable means for characterizing forest structure and distinguishing even from uneven-sized areas. The aim of this study was to achieve a thorough understanding on the relations between Airborne Laser Scanning (ALS) metrics and indicators based on Lorenz curve ordering: Gini coefficient (GC) and Lorenz asymmetry (S). Exploratory multivariate analysis was carried out using correlation tests, partial least squares (PLS), and an information-theoretic approach for multimodel inference (MMI). Best subset linear model was selected for GC and S prediction, as variable transformations yielded no improvement in the relation of ALS with the given response. Relative variable importance based on the MMI model showed that GC is best predicted by ALS metrics expressing canopy coverage, return dispersion, and low–high percentile combinations. Although ALS metrics showed no correlation with S, they did so against its consti...
Juha Hyyppä - One of the best experts on this subject based on the ideXlab platform.
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single sensor solution to tree species classification using multispectral Airborne Laser Scanning
Remote Sensing, 2017Co-Authors: Juha Hyyppä, Mikko Vastaranta, Harri Kaartinen, Paula Litkey, Markus HolopainenAbstract:This paper investigated the potential of multispectral Airborne Laser Scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensorsolution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping.
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fully automated power line extraction from Airborne Laser Scanning point clouds in forest areas
Remote Sensing, 2014Co-Authors: Lingli Zhu, Juha HyyppäAbstract:High-voltage power lines can be quite easily mapped using Laser Scanning data, because vegetation close to high-voltage lines is typically removed and also because the power lines are located higher off the ground in contrast to regional networks and lower voltage networks. On the contrary, lower voltage power lines are located in the middle of dense forests, and it is difficult to classify power lines in such an environment. This paper proposes an automated power line detection method for forest environments. Our method was developed based on statistical analysis and 2D image-based processing technology. During the process of statistical analysis, a set of criteria (e.g., height criteria, density criteria and histogram thresholds) is applied for selecting the candidates for power lines. After transforming the candidates to a binary image, image-based processing technology is employed. Object geometric properties are considered as criteria for power line detection. This method was conducted in six sets of Airborne Laser Scanning (ALS) data from different forest environments. By comparison with reference data, 93.26% of power line points were correctly classified. The advantages and disadvantages of the methods were analyzed and discussed.
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Airborne Laser Scanning and digital stereo imagery measures of forest structure comparative results and implications to forest mapping and inventory update
Canadian Journal of Remote Sensing, 2013Co-Authors: Mikko Vastaranta, Juha Hyyppä, Joanne C White, Michael A Wulder, Markus Holopainen, C. Ginzler, Sakari Tuominen, Anssi Pekkarinen, Ville Kankare, Hannu HyyppaAbstract:Airborne Laser Scanning (ALS) has demonstrated utility for forestry applications and has renewed interest in other forms of remotely sensed data, especially those that capture three-dimensional (3-D) forest characteristics. One such data source results from the advanced processing of high spatial resolution digital stereo imagery (DSI) to generate 3-D point clouds. From the derived point cloud, a digital surface model and forest vertical information with similarities to ALS can be generated. A key consideration is that when developing forestry related products such as a canopy height model (CHM), a high spatial resolution digital terrain model (DTM), typically from ALS, is required to normalize DSI elevations to heights above ground. In this paper we report on our investigations into the use of DSI-derived vertical information for capturing variations in forest structure and compare these results to those acquired using ALS. An ALS-derived DTM was used to provide the spatially detailed ground surface elev...
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advances in forest inventory using Airborne Laser Scanning
Remote Sensing, 2012Co-Authors: Juha Hyyppä, Hannu Hyyppa, Mikko Vastaranta, Markus Holopainen, Antero Kukko, Harri Kaartinen, Anttoni Jaakkola, Matti Vaaja, J Koskinen, Petteri AlhoAbstract: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.
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comparing accuracy of Airborne Laser Scanning and terrasar x radar images in the estimation of plot level forest variables
Remote Sensing, 2010Co-Authors: Markus Holopainen, Juha Hyyppä, Mikko Vastaranta, Reija Haapanen, Mika Karjalainen, Sakari Tuominen, Hannu HyyppaAbstract:Abstract: In this study we compared the accuracy of low-pulse Airborne Laser Scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV) Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA). The nonparametric k -nearest neighbour ( k -NN) algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained
Markus Holopainen - One of the best experts on this subject based on the ideXlab platform.
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single sensor solution to tree species classification using multispectral Airborne Laser Scanning
Remote Sensing, 2017Co-Authors: Juha Hyyppä, Mikko Vastaranta, Harri Kaartinen, Paula Litkey, Markus HolopainenAbstract:This paper investigated the potential of multispectral Airborne Laser Scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensorsolution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping.
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Airborne Laser Scanning and digital stereo imagery measures of forest structure comparative results and implications to forest mapping and inventory update
Canadian Journal of Remote Sensing, 2013Co-Authors: Mikko Vastaranta, Juha Hyyppä, Joanne C White, Michael A Wulder, Markus Holopainen, C. Ginzler, Sakari Tuominen, Anssi Pekkarinen, Ville Kankare, Hannu HyyppaAbstract:Airborne Laser Scanning (ALS) has demonstrated utility for forestry applications and has renewed interest in other forms of remotely sensed data, especially those that capture three-dimensional (3-D) forest characteristics. One such data source results from the advanced processing of high spatial resolution digital stereo imagery (DSI) to generate 3-D point clouds. From the derived point cloud, a digital surface model and forest vertical information with similarities to ALS can be generated. A key consideration is that when developing forestry related products such as a canopy height model (CHM), a high spatial resolution digital terrain model (DTM), typically from ALS, is required to normalize DSI elevations to heights above ground. In this paper we report on our investigations into the use of DSI-derived vertical information for capturing variations in forest structure and compare these results to those acquired using ALS. An ALS-derived DTM was used to provide the spatially detailed ground surface elev...
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advances in forest inventory using Airborne Laser Scanning
Remote Sensing, 2012Co-Authors: Juha Hyyppä, Hannu Hyyppa, Mikko Vastaranta, Markus Holopainen, Antero Kukko, Harri Kaartinen, Anttoni Jaakkola, Matti Vaaja, J Koskinen, Petteri AlhoAbstract: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.
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comparing accuracy of Airborne Laser Scanning and terrasar x radar images in the estimation of plot level forest variables
Remote Sensing, 2010Co-Authors: Markus Holopainen, Juha Hyyppä, Mikko Vastaranta, Reija Haapanen, Mika Karjalainen, Sakari Tuominen, Hannu HyyppaAbstract:Abstract: In this study we compared the accuracy of low-pulse Airborne Laser Scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV) Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA). The nonparametric k -nearest neighbour ( k -NN) algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained
Hakan Olsson - One of the best experts on this subject based on the ideXlab platform.
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estimation of stem attributes using a combination of terrestrial and Airborne Laser Scanning
European Journal of Forest Research, 2012Co-Authors: Eva Lindberg, Johan Holmgren, Kenneth Olofsson, Hakan OlssonAbstract:Properties of individual trees can be estimated from Airborne Laser Scanning (ALS) data provided that the Scanning is dense enough and the positions of field-measured trees are available as training data. However, such detailed manual field measurements are laborious. This paper presents new methods to use terrestrial Laser Scanning (TLS) for automatic measurements of tree stems and to further link these ground measurements to ALS data analyzed at the single tree level. The methods have been validated in six 80 × 80 m field plots in spruce-dominated forest (lat. 58°N, long. 13°E). In a first step, individual tree stems were automatically detected from TLS data. The root mean square error (RMSE) for DBH was 38.0 mm (13.1 %), and the bias was 1.6 mm (0.5 %). In a second step, trees detected from the TLS data were automatically co-registered and linked with the corresponding trees detected from the ALS data. In a third step, tree level regression models were created for stem attributes derived from the TLS data using independent variables derived from trees detected from the ALS data. Leave-one-out cross-validation for one field plot at a time provided an RMSE for tree level ALS estimates trained with TLS data of 46.0 mm (15.4 %) for DBH, 9.4 dm (3.7 %) for tree height, and 197.4 dm3 (34.0 %) for stem volume, which was nearly as accurate as when data from manual field inventory were used for training.
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prediction of stem attributes by combining Airborne Laser Scanning and measurements from harvesters
Silva Fennica, 2012Co-Authors: Johan Holmgren, Andreas Barth, Henrik Larsson, Hakan OlssonAbstract:Holmgren, J., Barth, A., Larsson, H. & Olsson, H. 2012. Prediction of stem attributes by combining Airborne Laser Scanning and measurements from harvesters. Silva Fennica 46(2): 227–239. In this study, a new method was validated for the first time that predicts stem attributes for a forest area without any manual measurements of tree stems by combining harvester measurements and Airborne Laser Scanning (ALS) data. A new algorithm for automatic segmentation of tree crowns from ALS data based on tree crown models was developed. The test site was located in boreal forest (64o06’N, 19o10’E) dominated by Norway spruce (Picea abies) and Scots Pine (Pinus sylvestris).The trees were harvested on field plots, and each harvested tree was linked to the nearest tree crown segment derived from ALS data. In this way, a reference database was created with both stem data from the harvester and ALS derived features for linked tree crowns. To estimate stem attributes for a tree crown segment in parts of the forest where trees not yet have been harvested, tree stems are imputed from the most similar crown segment in the reference database according to features extracted from ALS data. The imputation of harvester data was validated on a sub-stand-level, i.e. 2–4 aggregated 10 m radius plots, and the obtained RMSE of stem volume, mean tree height, mean stem diameter, and stem density (stems per ha) estimates were 11%, 8%, 12%, and 19%, respectively. The imputation of stem data collected by harvesters could in the future be used for bucking simulations of not yet harvested forest stands in order to predict wood assortments.
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estimation of tree height and stem volume on plots using Airborne Laser Scanning
Forest Science, 2003Co-Authors: Johan Holmgren, Mats Nilsson, Hakan OlssonAbstract:Airborne Laser Scanning has the ability to measure the vertical and horizontal structure of forest vegetation. The aim of this study is to investigate how measures derived from Laser Scanning data can be used in regression models for estimation of basal-areaweighted mean tree height and stem volume on 10 m radius field plots. Influence of Laser scan angle on tree height estimation and on crown coverage area estimation is also investigated. The study area was located in southern Sweden (lat. 58°30'N, long. 13°40'E). The dominating tree species were Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). Linear regression functions (R 2 =0.89-0.91) used to predict basal-areaweighted mean tree height had a Root Mean Square Error (RMSE) of1.45-1.56 m, corresponding to 10-11% of average height. Scanning angle was not significant for estimation of basal-area-weighted mean tree height. Two regression models were used for prediction of stem volume. The first model (R 2 = 0.90), with Laser derived mean height together with Laser derived crown coverage area as predicting variables, gave RMSE of 37 m 3 ha -1 , corresponding to 22% of average stem volume. The second model (R 2 =0.82), with Laser derived tree height together with Laser derived stem number as predicting variables, gave RMSEof 43 m 3 ha -1 , corresponding to 26% of average stem volume. The results implies that Airborne Laser Scanning, if combined with a field sample, has potential of retrieving information with high spatial resolution (10 m radius plot) about tree height and stem volume for a forest area.
