This paper introduces an integrative approach to hedonic house price modeling which utilizes high density 3D airborne laser scanning (ALS) data. In general, it is shown that extracting exploratory variables using 3D analysis – thus explicitly considering high-rise buildings, shadowing effects, etc. – is crucial in complex urban environments and is limited in well-established raster-based modeling. This is fundamental in large-scale urban analyses where essential determinants influencing real estate prices are constantly missing and are not accessible in official and mass appraiser databases. More specifically, the advantages of this methodology are demonstrated by means of a novel and economically important externality, namely incoming solar radiation, derived separately for each flat. Findings from an empirical case study in Vienna, Austria, applying a non-linear generalized additive hedonic model, suggest that solar radiation is significantly capitalized in flat prices. A model comparison clearly proves that the hedonic model accounting for ALS-based solar radiation performs significantly superior. Compared to a model without this externality, it increases the model’s explanatory power by approximately 13% and additionally reduces the prediction error by around 15%. The results provide strong evidence that explanatory variables originating from ALS, explicitly regarding the immediate 3D surroundings, enhance traditional hedonic models in urban environments.

Terrestrial Laser Scanning (TLS) is well suited to acquire high resolution point clouds, which can be used to derive single tree attributes for forestry applications. The processing of TLS point clouds requires the implementation of automated processing chains allowing operational data analysis in 3D. In order to obtain detailed information such as canopy structure, biomass and leaf area index and their respective changes over time the extracted branch structure of the tree is of particular interest. Based on TLS-point clouds a variety of procedures and workflows have been developed in order to extract branch parameters. Most approaches are limited by noisy data and inhomogeneous coverage within the canopy. This is mainly the case under leaf-on conditions were most branch segments are masked or under sampled. We present an experimental framework for an automated processing chain for structuring and classifying TLS point cloud data into branches and foliage. The aim is the generation of improved input data for further sophisticated processing procedures such as skeletonization. By separating branches from leafs an improved input for branch hierarchy generation is expected. Branch extraction was done for an exemplary tree data set, which was acquired in summer (leaf-on) and winter (leaf-off) conditions respectively. First, segmentation of tree components inside of the TLS point cloud was performed. For each derived segment a corresponding structure tensor was computed, whereby eigenvectors and eigenvalues were derived. Applying a form-index describing the eigenvalue relationship for each segment, a classification into segment shapes has been done. The quality of the extraction was tested under leaf-on and leaf-off conditions. Therefore, derived branch structures were analysed. It could be shown that major branch structures are derivable in both leaf-on and leaf-off conditions whereby small branches are more difficult to extract under leafon conditions. The algorithm shows good results in the reduction of noise from the data. However, large data gaps within the main branch system mainly due to occlusion in dense leaf-on canopy cannot be overcome by the current approach. Concluding, the presented approach cannot solve the problem of data gaps, but sufficiently unmasks branch structures of noisy data sets. It uses branch segments instead of point or voxel neighbourhoods for data representation and is a promising pre-processing approach for following procedures such as skeletonization and tree modelling of dense point clouds.

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

Most algorithms performing segmentation of 3D point cloud data acquired by, e.g. Airborne Laser Scanning (ALS) systems are not suitable for large study areas because the huge amount of point cloud data cannot be processed in the computer’s main memory. In this study a new workflow for seamless automated roof plane detection from ALS data is presented and applied to a large study area. The design of the workflow allows area-wide segmentation of roof planes on common computer hardware but leaves the option open to be combined with distributed computing (e.g. cluster and grid environments). The workflow that is fully implemented in a Geographical Information System (GIS) uses the geometrical information of the 3D point cloud and involves four major steps: (i) The whole dataset is divided into several overlapping subareas, i.e. tiles. (ii) A raster based candidate region detection algorithm is performed for each tile that identifies potential areas containing buildings. (iii) The resulting building candidate regions of all tiles are merged and those areas overlapping one another from adjacent tiles are united to a single building area. (iv) Finally, three dimensional roof planes are extracted from the building candidate regions and each region is treated separately. The presented workflow reduces the data volume of the point cloud that has to be analyzed significantly and leads to the main advantage that seamless area-wide point cloud based segmentation can be performed without requiring a computationally intensive algorithm detecting and combining segments being part of several subareas (i.e. processing tiles). A reduction of 85% of the input data volume for point cloud segmentation in the presented study area could be achieved, which directly decreases computation time.

In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means of biomass expansion factors. Furthermore, the semi-empirical model is extended by three different canopy transparency parameters derived from airborne LiDAR data. These parameters have not been considered for stem volume estimation until now and are introduced in order to investigate the behavior of the model concerning AGB estimation. The developed additional input parameters are based on the assumption that transparency of vegetation can bemeasured by determining the penetration of the laser beams through the canopy. These parameters are calculated for every single point within the 3D point cloud in order to consider the varying properties of the vegetation in an appropriate way. Exploratory Data Analysis (EDA) is performed to evaluate the influence of the additional LiDAR derived canopy transparency parameters for AGB estimation. The study is carried out in a 560 km 2 alpine area in Austria, where reference forest inventory data and LiDAR data are available. The investigations show that the introduction of the canopy transparency parameters does not change the results significantly according to R-2 (R-2 = 0.70 to R-2 = 0.71) in comparison to the results derived from, the semi-empirical model, which was originally developed for stem volume estimation.

In recent years there has been an increasing demand among home owners for cost effective sustainable energy production such as solar energy to provide heating and electricity. A lot of research has focused on the assessment of the incoming solar radiation on roof planes acquired by, e.g., Airborne Laser Scanning (ALS). However, solar panels can also be mounted on building facades in order to increase renewable energy supply. Due to limited reflections of points from vertical walls, ALS data is not suitable to perform solar potential assessment of vertical building facades. This paper focuses on a new method for automatic solar radiation modeling of facades acquired by Mobile Laser Scanning (MLS) and uses the full 3D information of the point cloud for both the extraction of vertical walls covered by the survey and solar potential analysis. Furthermore, a new method isintroduced determining the interior and exterior face, respectively, of each detected wall in order to calculate its slope and aspect angles that are of crucial importance for solar potential assessment. Shadowing effects of nearby objects are considered by computing the 3D horizon of each point of a facade segment within the 3D point cloud.

Most solar radiation models implemented in Geographical Information Systems (GIS) operate on 2.5D raster data. There are only a few models using the full 3D information of a point cloud obtained by e.g. Airborne Laser Scanning (ALS) systems. However, models performing point cloud based solar radiation modeling are not suitable for large study areas because the huge amount of point cloud data cannot be handled in one go in the computer's main memory. Deconstructing the whole dataset into several tiles requires the usage of buffer areas surrounding each tile to avoid edge effects when calculating necessary features such as slope and aspect as well as shadowing effects of nearby objects within the point cloud. In this paper a point cloud based solar radiation model that is fully implemented in SAGA GIS is presented and applied to large areas.

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

A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An object-based error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation ( e. g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m(2).

In times of higher market prices of fossil fuels and to meet the increasingly environmental and economic threads of climate change renewable energy must play a major role for global energy supply. This paper focuses on a new method for fully automated solar potential assessment of roof planes from airborne LiDAR data and uses the full 3D information for both, roof plane detection and solar potential analysis. An image based candidate region detection algorithm reduces the data volume of the point cloud and identifies potential areas containing buildings with high completeness (97%). Three dimensional roof planes are extracted from the building candidate regions and their aspect and slope are calculated. The horizon of each roof plane is calculated within the 3D point cloud and thus shadowing effects of nearby objects such as vegetation, roofs, chimneys, dormers etc. are respected in a proper way. In contrast to other objects such as walls or buildings vegetation is cha racterized by transparent properties. Thus, in a further st ep vegetation is detected within the remaining non-roof points and transparent shadow values are introduced by calculating a local transparency measure averaged per tree segment. The following solar potential analysis is performed for regularly distributed roof points a nd results in both, (i) the annual sum of the direct and diffuse radiation for eac h roof plane and (ii) in a detailed information about the dist ribution of radiation within one roof. By calculating a clear sky index, cloud cover effects are considered using data from a nearby meteorological ground station.