Abstract. Graffiti is a short-lived form of heritage balancing between tangible and intangible, offensive and pleasant. Graffiti makes people laugh, wonder, angry, think. These conflicting traits are all present along Vienna's Donaukanal (Eng. Danube Canal), a recreational hotspot – located in the city's heart – famous for its endless display of graffiti. The graffiti-focused heritage science project INDIGO aims to build the basis to systematically document, monitor, and analyse circa 13 km of Donaukanal graffiti in the next decade. The first part of this paper details INDIGO's goals and overarching methodological framework, simultaneously placing it into the broader landscape of graffiti research. The second part of the text concentrates on INDIGO's graffiti documentation activities. Given the project's aim to create a spatially, spectrally, and temporally accurate record of all possible mark-makings attached in (il)legal ways to the public urban surfaces of the Donaukanal, it seems appropriate to provide insights on the photographic plus image-based modelling activities that form the foundation of INDIGO's graffiti recording strategy. The text ends with some envisioned strategies to streamline image acquisition and process the anticipated hundreds of thousands of images.

Mobile mapping is in the process of becoming a routinely applied standard tool to support administration of cities. For ensuring the usability of the mobile mapping data it is necessary to have a practical method to evaluate the quality of different systems, which reaches beyond 3D accuracy of individual points. Such a method must be objective, easy to implement, and provide quantitative results to be used in tendering processes. We present such an approach which extracts quality figures for point density, point distribution, point cloud planarity, image resolution, and street sign legibility. In its practical application for the mobile mapping campaign of the City of Vienna (Austria) in 2020 the proposed test method proved to fulfill the above requirements. As an additional result, quality figures are reported for the panorama images and point clouds of three different mobile mapping systems.

In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types, and structures. This is the first benchmark ever performed for different forests within the Alps. The evaluation of the detection results was carried out in a reproducible way by automatically matching them to precise in situ forest inventory data using a restricted nearest neighbor detection approach. Quantitative statistical parameters such as percentages of correctly matched trees and omission and commission errors are presented. The proposed automated matching procedure presented herein shows an overall accuracy of 97%. Method based analysis, investigations per forest type, and an overall benchmark performance are presented. The best matching rate was obtained for single-layered coniferous forests. Dominated trees were challenging for all methods. The overall performance shows a matching rate of 47%, which is comparable to results of other benchmarks performed in the past. The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.

In the project NEWFOR, financed by the European Territorial Cooperation "Alpine Space", new remote sensing technologies (LiDAR & UAV) for a better mountain forest timber mobilization were investigated. In this contribution possibilities and limitations for detecting forest area and forest biomass as well as the accessibility of forest are shown and discussed. The method for forest area delineation is fully automatically, can be applied to large areas and fulfils the requirements of an operational application. Different forest definitions can be considered by this method. Therefore, an application to different countries with different forest definitions is enabled. For the biomass estimation a semi-empirical regression model was used. The derived biomass maps have a very high spatial resolution and allow comprehensive forest management for large areas and serve as input data for various forest planning activities. It could also be shown that multi-temporal LiDAR data is an excellent data source for change detection of forest parameters (i.e. forest area, biomass). Finally an approach for deriving the forest road network including the road properties width, radius and inclination is presented. The investigations are done within several study areas distributed over the entire Alpine region. Several LiDAR data sets with different properties are analysed and demonstrate their practical relevance in forestry.

This study presents a new open-access dataset of 18 plots from alpine forests of the Alpine Space. Eight detection algorithms were tested and evaluated against forest inventory data using a novel, automated matching procedure. Forest structure remains a key issue limiting tree detection, and algorithms would probably benefit from an adaptive tuning in order to achieve a better trade-off between omission and commission errors.

The Forest in the alpine space can be linked to different ecological and economical functions in our society. For example many habitats and species are hosted by the forest which can be related to an ecological function. From an economic point of view forest in the alpine space represents a key resource within this mountainous environment. The valorization of this resource is often limited by accessibility constraints that prevent an efficient mapping, management, harvesting and transport of wood products. From an economical perspective, the production of renewable resources like timber and fuelwood has positive effects both at global scale, with climate change mitigation, and local scale with rural employment and the development of a regional value chain. Therefore the objective of preserving and improving the development of alpine forests is a point of public interest. However, managing forests in mountainous territories is a difficult task as topography and climate set strong constraints inside a complex socio-economical framework. In particular, a precise mapping of forest biomass characteristics and mobilization conditions (harvesting and accessibility) is a prerequisite for implementing an efficient supply chain for the wood industry. The available information is currently insufficient to provide, at reasonable costs, the required guarantees on the wood supply and on its sustainability. With recent developments in the remote sensing domain as for example enhanced sensor technologies and improved modelling tools, major improvements regarding the evaluation of the forest growing stock and accessibility are now possible. Upon this highly valuable information, decision-making tools are needed to optimize the investments in forest infrastructures required for a cost-effective wood supply while securing the sustainable management of forests.

Individual tree crowns can be delineated from dense airborne laser scanning (ALS) data and their species can be classified from the spatial distribution and other variables derived from the ALS data within each tree crown. This study reports a new clustering approach to delineate tree crowns in three dimensions (3-D) based on ellipsoidal tree crown models (i.e., ellipsoidal clustering). An important feature of this approach is the aim to derive information also about the understory vegetation. The tree crowns are delineated from echoes derived from full-waveform (fwf) ALS data as well as discrete return ALS data with first and last returns. The ellipsoidal clustering led to an improvement in the identification of tree crowns. Fwf ALS data offer the possibility to derive also the echo width and the amplitude in addition to the 3-D coordinates of each echo. In this study, tree species are classified from variables describing the fwf (i.e., the mean and standard deviation of the echo amplitude, echo width, and total number of echoes per pulse) and the spatial distribution of the clusters for pine, spruce, birch, oak, alder, and other species. Supervised classification is done for 68 field plots with leave-one-out cross-validation for one field plot at a time. The total accuracy was 71% when using both fwf and spatial variables, 60% when using only spatial variables, and 53% when using discrete return data. The improvement was greatest for discriminating pine and spruce as well as pine and birch.

Extracting 3D tree models based on terrestrial laser scanning (TLS) point clouds is a challenging task as trees are complex objects. Current TLS devices acquire high-density data that allow a detailed reconstruction of the tree topology. However, in dense forests a fully automatic reconstruction of trees is often limited by occlusion, wind influences and co-registration issues. In this paper, a semi-automatic method for extracting branching and stem structure based on equirectangular projections (range and intensity maps) is presented. The digitization of branches and stems is based on 2D maps, which enables simple navigation and raster processing. The modeling is performed for each viewpoint individually instead of using a registered point cloud. Previously reconstructed 2D-skeletons are transformed between the maps. Therefore, wind influences, orientation imperfections of scans and data gaps can be overcome. The method is applied to a TLS dataset acquired in a forest in Germany. In total 34 scans were carried out within a managed forest to measure approximately 90 spruce trees with minimal occlusions. The results demonstrate the feasibility of the presented approach to extract tree models with a high completeness and correctness and provide an excellent input for further modeling applications.

This paper presents a methodology for the derivation of structural parameters and stem volume in forests based on Airborne Laser Scanning (ALS) data. We describe three different measures of horizontal and vertical canopy structure: (1) tree crown segmentation, (2) compactness of vegetation patches, and (3) vertical layering of vegetation patches and canopy cover. An empirical regression model for the derivation of stem volume from the ALS and forest inventory sample plot data is described and its results are validated with extensive reference data. Different study areas in Austria were used to illustrate the workflows. The presented study demonstrates the applicability of the proposed methods on study sites and ALS data of differing characteristics, as well as it points out the suitability of ALS as a tool for reliable wide area assessment of structural parameters and stem volume for forested areas.

The delineation of forested areas is a critical task, because the resulting maps are a fundamental input for a broad field of applications and users. Different national and international forest definitions are available for manual or automatic delineation, but unfortunately most definitions lack precise geometrical descriptions for the different criteria. A mandatory criterion in forest definitions is the criterion of crown coverage (CC), which defines the proportion of the forest floor covered by the vertical projection of the tree crowns. For loosely stocked areas, this criterion is especially critical, because the size and shape of the reference area for calculating CC is not clearly defined in most definitions. Thus current forest delineations differ and tend to be non-comparable because of different settings for checking the criterion of CC in the delineation process. This paper evaluates a new approach for the automatic delineation of forested areas, based on airborne laser scanning (ALS) data with a clearly defined method for calculating CC. The new approach, the 'tree triples' method, is based on defining CC as a relation between the sum of the crown areas of three neighboring trees and the area of their convex hull. The approach is applied and analyzed for two study areas in Tyrol, Austria. The selected areas show a loosely stocked forest at the upper timberline and a fragmented forest on the hillside. The fully automatic method presented for delineating forested areas from ALS data shows promising results with an overall accuracy of 96%, and provides a beneficial tool for operational applications.

• This paper evaluates a new approach for the automatic delineation of forested areas, based on airborne laserscanning data and criterions of the forest definition of the Austrian national forest inventory. • A new method for calculating the crown coverage, which is a mandatory criterion in every forest definition, is introduced. The ‘tree triples’ method, is based on defining CC as a relation between the sum of the crown areas of three neighboring trees and the area of their convex hull. • The method presented delivers repeatable and objective results. Compared to a manually delineated reference mask, the method presented delivers a Kappa of 0.92 and an overall accuracy of 96 %. • The results of the approach show the high potential of a fully automatic delineation of forested areas, based on airborne laser scanning.

This paper presents a methodology for the derivation of structural parameters and stem volume in forests based on Airborne Laser Scanning (ALS) data. We describe three different measures of horizontal and vertical canopy structure: (1) tree crown segmentation, (2) compactness of vegetation patches, and (3) vertical layering of vegetation patches and canopy cover. An empirical regression model for the derivation of stem volume from the ALS and forest inventory sample plot data is described and its results are validated with extensive reference data. Different study areas in Austria were used to illustrate the workflows. The presented study demonstrates the applicability of the proposed methods on study sites and ALS data of differing characteristics, as well as it points out the suitability of ALS as a tool for reliable wide area assessment of structural parameters and stem volume for forested areas. Resumo Esse artigo apresenta uma metodologia para derivação de parâmetros estruturais e de volume de madeira em florestas baseado em dados de Laser Scanner Aerotransportado (ALS). Nós descrevemos três diferentes medidas da estrutura horizontal e vertical da copa: (1) segmentação da copa da árvore, (2) compacidade das manchas de vegetação, (3) estratificação vertical das manchas de vegetação e cobertura do dossel. Um modelo empírico de regressão para derivar o volume de madeira fazendo uso de dados ALS e dados amostrais obtidos em inventário florestal é descrito e seus resultados são validados com extensivos dados de referência. Diferentes áreas na Áustria foram utilizadas para ilustrar o fluxo de trabalho. O estudo apresentado demonstra a aplicabilidade dos métodos propostos nas áreas de estudo e dos dados ALS de diferentes características, bem como aponta a adequação do ALS como ferramenta confiável para avaliação de parâmetros de estrutura e de volume de madeira de amplas áreas florestais.

As an active remote sensing system airborne laser scanning (ALS) is well suited to achieve normalized digital surface models (nDSMs) by subtracting digital terrain models (DTMs) from digital surface models (DSMs). The nDSM represents object heights and is an important data source for the derivation of various forest parameters such as tree height, stem volume or biomass. The validation of the derived results as well as the comparison of the results from different study areas is often a challenging task due to different sampling designs and accuracies of forest inventory data being used as ground truth data. In this study we use 17 fully callipered samples, covering an area of 4.55 ha in total, to assess the accuracies of stem volume and biomass maps for different Austrian test sites. For the callipered samples all trees with a diameter at breast height ≥10.5 cm were measured. For the calibration of the stem volume and biomass models available national forest inventory (NFI) as well as local forest inventory (LFI) data are used, which are both based on angle count sampling plots. This verification approach guarantees firstly the independency of calibration and validation data and secondly it allows accuracy analyses for different reference units. For the study area Montafon the relative differences of stem volume and biomass range between -20.0% and 57.4% and between 16.3% and 56.2% respectively for twelve coniferous dominated sample areas with ~0.25 ha each. For a reference unit with an area of ~3.0 ha the relative differences decrease to 15.7% and 19.3% for stem volume and biomass respectively. For the study area Tyrol deciduous and coniferous models were applied. The calculated relative differences of stem volume and biomass vary between -25.8% and -10.3% and -18.5% and 3.1% respectively for the two coniferous dominated sample areas with an area of ~0.38 ha each. For the two deciduous dominated sample areas with an area of ~0.38 ha, both the relative difference of stem volume and biomass vary between -10.0% and 0.6% and -3.3% and 1.1% respectively. The average relative differences for all sample areas of the Tyrol study area with a total area of ~1.5 ha is -1.2% and -1.9% for the stem volume and the biomass, respectively. As the estimations of the stem volume and biomass maps are based on federal state wide data sets (ALS and NFI) the findings of this study are of high practical relevance for integrating ALS derived forest parameters into operational forest inventories. EARSeL eProceedings 11, 1/2012 75

In this contribution a method based on a two-stream radiative transfer model (Bohren, 1987) is presented to calculate two physical parameters derived from full-waveform airborne laser scanning (FWF-ALS) data, which have a close relation to the density and the reflectivity of the foliage and consequently to the tree species. Small-footprint FWF-ALS is an effective technology for acquiring 3D information of forested areas i.e. tree height, geometric distribution of leaves and branches. In opposite to tree heights the physical properties derived from FWF-ALS (i.e. amplitude, echo width, cross-section) are widely not used for operational forestry applications until now. In forest canopies the emitted laser beam interacts with multiple scatterers. It is assumed that the topmost objects (i.e. leaves, branches, needles) backscatter the first part of the laser light/energy (1 echo), whereas the decreased light travels further downwards until it is finally backscattered from underlying objects (2, 3, etc. echo). It is assumed that for different tree species a characteristic range of decrease of the light energy occurs. There are some models describing the loss of energy within the laser beam while penetrating the canopy. A theoretically correct method focusing on the geometry is the Monte-Carlo radiative transfer model (e.g. North et al., 2010). These calculations can model any measured echo as a function of a set of geometrical and spectral parameters of the canopy very precise. The disadvantage of this method is the great number of required model parameters whereas many of these parameters are unknown or not well known in advance. For the current study a more simple two-stream radiative transfer model (Bohren, 1987) is applied, that describes the downward and the upward radiation in a volumetric, scattering media (like a tree crown) that is illuminated from above. According to the two-stream radiative transfer model the upward radiation as a function of the height shows the characteristics of a ‘recovery curve’. The recovery curve is a negative exponential function with two parameters, the ‘mean free path’ and the ‘asymptote’. Both of these parameters have a physical meaning. The mean free path is a distance, where the intensity of the downward radiation decreases to 1/e part. The asymptote is the upward radiation of an infinitely thick media. To apply this more simple model on FWF-ALS data, all echoes from multiple shots are selected within a vertical cylinder (representing a tree crown or a group of trees of the same species) (Figure 1). Furthermore to be independent of the actual height of trees, the heights are normalized for each tree. To represent the contribution of individual scatterers to the upward radiation, the cross section for each echo is calculated (the cross section describes the backscattering ability of light by an object). The cross section values (Wagner et al., 2008) for each echo are sorted according to the normalized heights and furthermore the cumulative sum of cross sections (CSCS) is calculated top down. For the CSCS versus the normalized heights a minus exponential function is fitted and furthermore two parameters describing the function are deduced (Figure 2). The first parameter ‘mean free path’ of the simplified model is reciprocal correlated with the density of the forest canopy where a short/small mean free path parameter means dense canopy. The second parameter ‘asymptote’ is proportional to the effective reflectivity of the forest canopy. Figure 1. Spatial position of echos from a beech tree. The method was applied for a high density FWF-ALS dataset for an area in Austria, which is covered by coniferous and deciduous trees. For this area also a detailed forest inventory dataset is available. Within this area, for the main tree species (beech, larch and spruce) several trees were selected based on the forest inventory data and furthermore the processing steps described above were applied. The two determined parameters ‘mean free path’ and ‘asymptote’ for each tree were plotted in a scatter plot. For each investigated tree species a clear clustering of the derived parameters could be shown (Figure 3). For a final tree species classification for larger areas, the tree species specific parameters were applied on a tree crown level and the derived results were validated with additional forest inventory data. Figure 2. Cumulative sum of cross sections as a function of normalized height for 7 spruce samples. Black dots are cumulative sums, only blue circles were involved in the calculations. The red curve shows the fitted minus exponential curves. The the 'mean free path' defined by the slope of a curve relates to the density of the foliage and the asymptotic value of a curve relates to the reflectivity. Figure 3. The crossplot of 'cumulative sum of cross section' and 'mean free path' values, resulted for sample dataset trees. Spruce can be easily separeted using this crossplot, but for larch and beech only the 'cumulative sum of cross section values' differ.

The objective of this paper is to evaluate a new approach for the automatic delineation of forested areas based on airborne laser scanning (ALS) and national forest inventory (NFI) data. In the Austrian NFI a forest area is mainly defined with four fundamental criteria. One of these criteria, the so called “crown coverage”, is the most complex variable and therefore the main focus of this paper is on defining and implementing this criterion in an automatic process to delineate forested areas. Based on Austrian NFI data functions were determined for two different test sites in Austria, describing the criterion crown coverage as a relation between tree height and the distance between trees. Based on the ALS data an automatic method on the basis of adapting α-shapes was developed to link these functions to the ALS data. The approach was tested for two different test sites in Austria. For the first test site a tree species independent function was applied. The results of the delineated forest mask are validated with a reference forest mask which was manually delineated based on orthophotos. The derived forest mask differ less than 1.6% from the reference forest mask and shows a very high accuracy. For the second test site tree species dependent functions were applied for the assessment of the crown coverage. The presented approach shows promising results and shows the high potential for the automatic forest area delineation based on ALS data.

a) Institute of Photogrammetry and Remote Sensing, Vienna University of Technology Gushausstrase 27-29, 1040 Wien, Austria b) Department of Forest Inventory at the Federal Research and Training Center for Forests, Natural Hazards and Landscape, Seckendorff-Gudent-Weg, 1130 Vienna, Austria c) alpS-Center for Natural Hazard Management, Grabenweg 3, 6020 Innsbruck, Austria d) LASERDATA GmbH Management and Analysis of Laserscanning Data, Technikerstr. 21a, 6020 Innsbruck, Austria e) Stand Montafon Forstfonds, Montafonerstrase 21, 6780 Schruns, Austria

Airborne laser scanning (ALS) data has been established as the standard method for the acquisition of high precision topographic data. In addition to the derivation of topographic models, such as digital terrain models (DTM) or digital surface models (DSM), ALS data is the main input data source for a variety of applications, e.g. building modelling, power line modelling or forestry applications. Until now a severe limitation is the availability of tools allowing computations directly on the 3D point cloud for district wide calculations. In complex 3D scenarios such as forests, the point cloud content is commonly converted to raster data (e.g. DTM and DSM) with a notable loss of information. As a result, the information on the vertical structure of vegetation is irretrievably lost. Therefore, a methodology for the delineation of forest areas and subsequent derivation of vertical vegetation strata is proposed. The presented approach combines processing steps directly in the 3D point cloud and in the raster domain using the software system OPALS (Orientation and Processing of Airborne Laser Scanning data). A number of examples located in Austria are used to illustrate the workflows.