Markus Hollaus - Academia.edu (original) (raw)

Papers by Markus Hollaus

Managing Forest Ecosystems, 2013

Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data a... more Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data acquisition around the year 2004. These systems typically digitize the analogue backscattered echo of the emitted laser pulse with a high frequency. FWF digitization has the advantage of not limiting the number of echoes that are recorded for each individual emitted laser pulse. Studies utilizing FWF data have shown that more echoes are provided from reflections in the vegetation in comparison to discrete echo systems. To obtain geophysical metrics based on ALS data that are independent of a mission's flying height, acquisition time or sensor characteristics, the FWF amplitude values can be calibrated, which is an important requirement before using them in further classification tasks. Beyond that, waveform digitization provides an additional observable which can be exploited in forestry, namely the width of the backscattered pulse (i.e. echo width). An early application of FWF ALS was to improve ground and shrub echo identification below the forest canopy for the improvement of terrain modelling, which can be achieved using the discriminative capability of the amplitude and echo width in classification algorithms. Further studies indicate that accuracies can be increased for classification (e.g. species) and biophysical parameter extraction (e.g. diameter at breast height) for single-tree-and area-based methods by exploiting the FWF observables amplitude and echo width.

ABSTRACT: In this paper we describe an approach to classify forested areas based on their vertica... more ABSTRACT: In this paper we describe an approach to classify forested areas based on their vertical vegetation structure using Airborne Laser Scanning (ALS) data. Surface and terrain roughness are essential input parameters for modeling of natural hazards such as avalanches and floods whereas it is basically assumed that flow velocities decrease with increasing roughness.

Integrative analysis of remote sensing data and socioeconomic information enables the transition ... more Integrative analysis of remote sensing data and socioeconomic information enables the transition of land cover and urban structures into a detailed functional model of urban land use. In this paper object based image analysis is used to derive a classification of urban structures. The implementation of ALS (Airborne Laser Scanning) significantly enhances the classification of optical imagery both in terms of accuracy as well as automation.

Roughness is an important input parameter for modeling of natural hazards such as floods, rock fa... more Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept (i.e., ranging from fine-scale soil characteristics to description of understory and lower tree layer) various roughness raster products were derived from the original full-waveform airborne laser scanning (FWF-ALS) point cloud using two different types of roughness parameters, the surface roughness (SR) and the terrain roughness (TR). For the calculation of the SR, ALS terrain points within a defined height range to the terrain surface are considered. For the parameterization of the SR, two approaches are investigated. In the first approach, a geometric description by calculating the standard deviation of plane fitting residuals of terrain points is used. In the second one, the potential of the derived echo widths are analyzed for the parameterization of SR. The echo width is an indicator for roughness and the slope of the target. To achieve a comparable spatial resolution of both SR layers, the calculation of the standard deviation of detrended terrain points requires a higher terrain point density than the SR parameterization using the echo widths. The TR describes objects (i.e., point clusters) close but explicitly above the terrain surface, with 20 cm defined as threshold height value for delineation of the surface layer (i.e., forest floor layer). Two different empirically defined vegetation layers below the canopy layer were analyzed (TR I: 0.2 m to 1.0 m; TR II: 0.2 m to 3.0 m). A 1 m output grid cell size was chosen for all roughness parameters in order to provide consistency for further integration of high-resolution optical imagery. The derived roughness parameters were then jointly classified, together with a normalized Digital Surface Model (nDSM) showing the height of objects (i.e., trees) above ground. The presented approach enables the classification of forested areas in patches of different vegetation structure (e.g., varying soil roughness, understory, density of natural cover). For validation purposes in situ reference data were collected and cross-checked with the classification results, positively confirming the general feasibility of the proposed vertical concept of integrated roughness mapping on various vertical levels. Results can provide valuable input for forest mapping and monitoring, in particular with regard to natural hazard modeling.

Kurzfassung In dieser Arbeit wird ein hochauflösendes Satellitenbild (IKONOS 2) gemeinsam mit Las... more Kurzfassung In dieser Arbeit wird ein hochauflösendes Satellitenbild (IKONOS 2) gemeinsam mit Laser-Scanning-Daten objekt-orientiert analysiert und klassifiziert. Aus dem manuell nachbearbeiteten Ergebnis werden die Gebäude herausgefiltert und mittels eines neu entwickelten Algorithmus halb-automatisch generalisiert. Die resultierenden Objekte dienen als Basis für die Generierung eines 3D Stadtmodells, wobei die Höheninformation aus den Laser-Scanning-Daten gewonnen wurde.

Comparison of discrete and full-waveform ALS for dead wood detection

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013

Today, airborne laser scanning (ALS) is the standard method for detailed topographic data acquisi... more Today, airborne laser scanning (ALS) is the standard method for detailed topographic data acquisition, which can complement, or partly replace, other existing geo-data acquisition technologies, and open up new exciting areas of applications. With hydrology as the main driving force extensive ALS fl ight campaigns have been carried out since the disastrous fl oods 2002 in Austria and therefore, ALS

Small-footprint airborne laser scanning (ALS) is increasingly used in vegetation and forest relat... more Small-footprint airborne laser scanning (ALS) is increasingly used in vegetation and forest related applications. This paper explores the potential of full-waveform (FWF) ALS information (i.e. echo width and backscatter cross section) for tree species classification and forest structure parameterization. In order to obtain defined physical quantities, radiometric calibration of the recorded FWF data is performed by using a natural radiometric

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011

In this contribution the complexity of the vertical vegetation structure, based on dense airborne... more In this contribution the complexity of the vertical vegetation structure, based on dense airborne laser scanning (ALS) point cloud data (25 echoes/m 2 ), is analyzed to calculate vegetation roughness for hydraulic applications. Using the original 3D ALS point cloud, three levels of abstractions are derived (cells, voxels and connections) to analyze ALS data based on a 1x1 m 2 raster over the whole data set. A voxel structure is used to count the echoes in predefined detrended height levels within each cell. In general, it is assumed that the number of voxels containing echoes is an indicator for elevated objects and consequently for increased roughness. Neighboring voxels containing at least one data point are merged together to connections. An additional height threshold is applied to connect vertical neighboring voxels with a certain distance in between. Thus, the connections indicate continuous vegetation structures. The height of the surface near or lowest connection is an indicator for hydrodynamic roughness coefficients. For cells, voxels and connections the laser echoes are counted within the structure and various statistical measures are calculated. Based on these derived statistical parameters a rule-based classification is developed by applying a decision tree to assess vegetation types. Roughness coefficient values such as Manning's n are estimated, which are used as input for 2D hydrodynamic-numerical modeling. The estimated Manning's values from the ALS point cloud are compared with a traditional Manning's map. Finally, the effect of these two different Manning's n maps as input on the 2D hydraulics are quantified by calculating a height difference model of the inundated depth maps. The results show the large potential of using the entire vertical vegetation structure for hydraulic roughness estimation.

Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest

Remote Sensing, 2012

ABSTRACT This study compares methods to estimate stem volume, stem number and basal area from Air... more ABSTRACT This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58 degrees N, Long. 13 degrees E). The stem volume was estimated with five different regression models: one model based on height and density metrics from the ALS data derived from the whole field plot, two models based on similar combinations derived from 0.5 m raster cells, and two models based on canopy volumes from the ALS data. The best result was achieved with a model based on height and density metrics derived from 0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a model based on height and density metrics derived from the whole field plot (RMSE 41.9%). The stem number and the basal area were estimated with: (i) area-based regression models using height and density metrics from the ALS data; and (ii) single tree-based information derived from local maxima in a normalized digital surface model (nDSM) mean filtered with different conditions. The estimates from the regression model were more accurate (RMSE 52.7% for stem number and 21.5% for basal area) than those derived from the nDSM (RMSE 63.4%-91.9% and 57.0%-175.5%, respectively). The accuracy of the estimates from the nDSM varied depending on the filter size and the conditions of the applied filter. This suggests that conditional filtering is useful but sensitive to the conditions.

Flood delineation from synthetic aperture radar data with the help of a priori knowledge from historical acquisitions and digital elevation models in support of near-real-time flood mapping

Earth Resources and Environmental Remote Sensing/GIS Applications III, 2012

ABSTRACT The monitoring of flood events with synthetic aperture radar (SAR) sensors has attracted... more ABSTRACT The monitoring of flood events with synthetic aperture radar (SAR) sensors has attracted a considerable amount of attention during the last decade, owing to the growing interest in using spaceborne data in near-real time flood management. Most existing methods for classifying flood extent from SAR data rely on pure image processing techniques. In this paper, we propose a method involving a priori knowledge about an area taken from a multitemporal time series and a digital elevation model. A time series consisting of ENVISAT ASAR acquisitions was geocoded and coregistered. Then, a harmonic model was fitted to each pixel time series. The standardised residuals of the model were classified as flooded when exceeding a certain threshold value. Additionally, the classified flood extent was limited to flood-prone areas which were derived from a freely available DEM using the height above nearest drainage (HAND) index. Comparison with two different reference datasets for two different flood events showed that the approach yielded realistic results but underestimated the inundation extent. Among the possible reasons for this are the rather coarse resolution of 150 m and the sparse data coverage for a substantial part of the time series. Nevertheless, the study shows the potential for production of rapid overviews in near-real time in support of early response to flood crises.

International Journal of Applied Earth Observation and Geoinformation, 2015

Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in re... more Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in recent years. Most available algorithms typically focus on single-image techniques which do not take into account the backscatter signature of a land surface under non-flooded conditions. In this study, harmonic analysis of a multi-temporal time series of > 500 ENVISAT Advanced SAR (ASAR) scenes with a spatial resolution of 150 m is used to characterise the seasonality in backscatter under non-flooded conditions. Pixels which were inundated during a large-scale flood event during the summer 2007 floods of the River Severn (United Kingdom) showed strong deviations from normal seasonal behaviour as inferred from the harmonic model. The residuals were classified by means of an automatic threshold optimisation algorithm after masking out areas which are unlikely to be flooded using a topography-derived index. The results were validated against a reference dataset derived from high-resolution airborne imagery. For the water class, accuracies > 80 % were found for non-urban land uses. A slight underestimation of the reference flood extent can be seen, mostly due to the lower spatial resolution of the ASAR imagery. Finally, an outlook for the proposed algorithm is given in the light of the Sentinel-1 mission.

Remote Sensing, 2011

Roughness is an important input parameter for modeling of natural hazards such as floods, rock fa... more Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept (i.e., ranging from fine-scale soil characteristics to description of understory and lower tree layer) various roughness raster products were derived from the original full-waveform airborne laser scanning (FWF-ALS) point cloud using two different types of roughness parameters, the surface roughness (SR) and the terrain roughness (TR). For the calculation of the SR, ALS terrain points within a defined height range to the terrain surface are considered. For the parameterization of the SR, two approaches are investigated. In the first approach, a geometric description by calculating the standard deviation of plane fitting residuals of terrain points is used. In the second one, the potential of the derived echo widths are analyzed for the parameterization of SR. The echo width is an indicator for roughness and the slope of the target. To achieve a comparable spatial resolution of both SR layers, the calculation of the standard deviation of detrended terrain points requires a higher terrain point density than the SR parameterization using the echo widths. The TR describes objects (i.e., point clusters) close but explicitly above the terrain surface, with 20 cm defined as threshold height value for delineation of the surface layer (i.e., forest OPEN ACCESS Remote Sens. 2011, 3 504 floor layer). Two different empirically defined vegetation layers below the canopy layer were analyzed (TR I: 0.2 m to 1.0 m; TR II: 0.2 m to 3.0 m). A 1 m output grid cell size was chosen for all roughness parameters in order to provide consistency for further integration of high-resolution optical imagery. The derived roughness parameters were then jointly classified, together with a normalized Digital Surface Model (nDSM) showing the height of objects (i.e., trees) above ground. The presented approach enables the classification of forested areas in patches of different vegetation structure (e.g., varying soil roughness, understory, density of natural cover). For validation purposes in situ reference data were collected and cross-checked with the classification results, positively confirming the general feasibility of the proposed vertical concept of integrated roughness mapping on various vertical levels. Results can provide valuable input for forest mapping and monitoring, in particular with regard to natural hazard modeling.

Managing Forest Ecosystems, 2013

Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data a... more Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data acquisition around the year 2004. These systems typically digitize the analogue backscattered echo of the emitted laser pulse with a high frequency. FWF digitization has the advantage of not limiting the number of echoes that are recorded for each individual emitted laser pulse. Studies utilizing FWF data have shown that more echoes are provided from reflections in the vegetation in comparison to discrete echo systems. To obtain geophysical metrics based on ALS data that are independent of a mission's flying height, acquisition time or sensor characteristics, the FWF amplitude values can be calibrated, which is an important requirement before using them in further classification tasks. Beyond that, waveform digitization provides an additional observable which can be exploited in forestry, namely the width of the backscattered pulse (i.e. echo width). An early application of FWF ALS was to improve ground and shrub echo identification below the forest canopy for the improvement of terrain modelling, which can be achieved using the discriminative capability of the amplitude and echo width in classification algorithms. Further studies indicate that accuracies can be increased for classification (e.g. species) and biophysical parameter extraction (e.g. diameter at breast height) for single-tree-and area-based methods by exploiting the FWF observables amplitude and echo width.

Forest Delineation Based on Airborne LIDAR Data

Remote Sensing, 2012

Sensors (Basel, Switzerland), 2011

In this study, a semi-empirical model that was originally developed for stem volume estimation is... more 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 be measured 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...

Österreichische Wasser- und Abfallwirtschaft, 2009

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

Individual tree crowns can be delineated from dense airborne laser scanning (ALS) data and their ... more 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.

Laser Radar Technology and Applications XII, 2007

Direct detection laser radar systems with echo signal digitization and subsequent full waveform a... more Direct detection laser radar systems with echo signal digitization and subsequent full waveform analysis provide additional information on the target's properties compared to conventional discrete echo systems. We focus on the advantages of utilizing the additional information especially in the course of airborne laser scanning, improving for example the mandatory process for classifying the measurement data for generating high-quality digital

The NEWFOR single tree detection benchmark–A test of LIDAR based detection methods using a unique dataset of different forest types within the alpine space

Managing Forest Ecosystems, 2013

Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data a... more Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data acquisition around the year 2004. These systems typically digitize the analogue backscattered echo of the emitted laser pulse with a high frequency. FWF digitization has the advantage of not limiting the number of echoes that are recorded for each individual emitted laser pulse. Studies utilizing FWF data have shown that more echoes are provided from reflections in the vegetation in comparison to discrete echo systems. To obtain geophysical metrics based on ALS data that are independent of a mission's flying height, acquisition time or sensor characteristics, the FWF amplitude values can be calibrated, which is an important requirement before using them in further classification tasks. Beyond that, waveform digitization provides an additional observable which can be exploited in forestry, namely the width of the backscattered pulse (i.e. echo width). An early application of FWF ALS was to improve ground and shrub echo identification below the forest canopy for the improvement of terrain modelling, which can be achieved using the discriminative capability of the amplitude and echo width in classification algorithms. Further studies indicate that accuracies can be increased for classification (e.g. species) and biophysical parameter extraction (e.g. diameter at breast height) for single-tree-and area-based methods by exploiting the FWF observables amplitude and echo width.

ABSTRACT: In this paper we describe an approach to classify forested areas based on their vertica... more ABSTRACT: In this paper we describe an approach to classify forested areas based on their vertical vegetation structure using Airborne Laser Scanning (ALS) data. Surface and terrain roughness are essential input parameters for modeling of natural hazards such as avalanches and floods whereas it is basically assumed that flow velocities decrease with increasing roughness.

Integrative analysis of remote sensing data and socioeconomic information enables the transition ... more Integrative analysis of remote sensing data and socioeconomic information enables the transition of land cover and urban structures into a detailed functional model of urban land use. In this paper object based image analysis is used to derive a classification of urban structures. The implementation of ALS (Airborne Laser Scanning) significantly enhances the classification of optical imagery both in terms of accuracy as well as automation.

Roughness is an important input parameter for modeling of natural hazards such as floods, rock fa... more Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept (i.e., ranging from fine-scale soil characteristics to description of understory and lower tree layer) various roughness raster products were derived from the original full-waveform airborne laser scanning (FWF-ALS) point cloud using two different types of roughness parameters, the surface roughness (SR) and the terrain roughness (TR). For the calculation of the SR, ALS terrain points within a defined height range to the terrain surface are considered. For the parameterization of the SR, two approaches are investigated. In the first approach, a geometric description by calculating the standard deviation of plane fitting residuals of terrain points is used. In the second one, the potential of the derived echo widths are analyzed for the parameterization of SR. The echo width is an indicator for roughness and the slope of the target. To achieve a comparable spatial resolution of both SR layers, the calculation of the standard deviation of detrended terrain points requires a higher terrain point density than the SR parameterization using the echo widths. The TR describes objects (i.e., point clusters) close but explicitly above the terrain surface, with 20 cm defined as threshold height value for delineation of the surface layer (i.e., forest floor layer). Two different empirically defined vegetation layers below the canopy layer were analyzed (TR I: 0.2 m to 1.0 m; TR II: 0.2 m to 3.0 m). A 1 m output grid cell size was chosen for all roughness parameters in order to provide consistency for further integration of high-resolution optical imagery. The derived roughness parameters were then jointly classified, together with a normalized Digital Surface Model (nDSM) showing the height of objects (i.e., trees) above ground. The presented approach enables the classification of forested areas in patches of different vegetation structure (e.g., varying soil roughness, understory, density of natural cover). For validation purposes in situ reference data were collected and cross-checked with the classification results, positively confirming the general feasibility of the proposed vertical concept of integrated roughness mapping on various vertical levels. Results can provide valuable input for forest mapping and monitoring, in particular with regard to natural hazard modeling.

Kurzfassung In dieser Arbeit wird ein hochauflösendes Satellitenbild (IKONOS 2) gemeinsam mit Las... more Kurzfassung In dieser Arbeit wird ein hochauflösendes Satellitenbild (IKONOS 2) gemeinsam mit Laser-Scanning-Daten objekt-orientiert analysiert und klassifiziert. Aus dem manuell nachbearbeiteten Ergebnis werden die Gebäude herausgefiltert und mittels eines neu entwickelten Algorithmus halb-automatisch generalisiert. Die resultierenden Objekte dienen als Basis für die Generierung eines 3D Stadtmodells, wobei die Höheninformation aus den Laser-Scanning-Daten gewonnen wurde.

Comparison of discrete and full-waveform ALS for dead wood detection

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013

Today, airborne laser scanning (ALS) is the standard method for detailed topographic data acquisi... more Today, airborne laser scanning (ALS) is the standard method for detailed topographic data acquisition, which can complement, or partly replace, other existing geo-data acquisition technologies, and open up new exciting areas of applications. With hydrology as the main driving force extensive ALS fl ight campaigns have been carried out since the disastrous fl oods 2002 in Austria and therefore, ALS

Small-footprint airborne laser scanning (ALS) is increasingly used in vegetation and forest relat... more Small-footprint airborne laser scanning (ALS) is increasingly used in vegetation and forest related applications. This paper explores the potential of full-waveform (FWF) ALS information (i.e. echo width and backscatter cross section) for tree species classification and forest structure parameterization. In order to obtain defined physical quantities, radiometric calibration of the recorded FWF data is performed by using a natural radiometric

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011

In this contribution the complexity of the vertical vegetation structure, based on dense airborne... more In this contribution the complexity of the vertical vegetation structure, based on dense airborne laser scanning (ALS) point cloud data (25 echoes/m 2 ), is analyzed to calculate vegetation roughness for hydraulic applications. Using the original 3D ALS point cloud, three levels of abstractions are derived (cells, voxels and connections) to analyze ALS data based on a 1x1 m 2 raster over the whole data set. A voxel structure is used to count the echoes in predefined detrended height levels within each cell. In general, it is assumed that the number of voxels containing echoes is an indicator for elevated objects and consequently for increased roughness. Neighboring voxels containing at least one data point are merged together to connections. An additional height threshold is applied to connect vertical neighboring voxels with a certain distance in between. Thus, the connections indicate continuous vegetation structures. The height of the surface near or lowest connection is an indicator for hydrodynamic roughness coefficients. For cells, voxels and connections the laser echoes are counted within the structure and various statistical measures are calculated. Based on these derived statistical parameters a rule-based classification is developed by applying a decision tree to assess vegetation types. Roughness coefficient values such as Manning's n are estimated, which are used as input for 2D hydrodynamic-numerical modeling. The estimated Manning's values from the ALS point cloud are compared with a traditional Manning's map. Finally, the effect of these two different Manning's n maps as input on the 2D hydraulics are quantified by calculating a height difference model of the inundated depth maps. The results show the large potential of using the entire vertical vegetation structure for hydraulic roughness estimation.

Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest

Remote Sensing, 2012

ABSTRACT This study compares methods to estimate stem volume, stem number and basal area from Air... more ABSTRACT This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58 degrees N, Long. 13 degrees E). The stem volume was estimated with five different regression models: one model based on height and density metrics from the ALS data derived from the whole field plot, two models based on similar combinations derived from 0.5 m raster cells, and two models based on canopy volumes from the ALS data. The best result was achieved with a model based on height and density metrics derived from 0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a model based on height and density metrics derived from the whole field plot (RMSE 41.9%). The stem number and the basal area were estimated with: (i) area-based regression models using height and density metrics from the ALS data; and (ii) single tree-based information derived from local maxima in a normalized digital surface model (nDSM) mean filtered with different conditions. The estimates from the regression model were more accurate (RMSE 52.7% for stem number and 21.5% for basal area) than those derived from the nDSM (RMSE 63.4%-91.9% and 57.0%-175.5%, respectively). The accuracy of the estimates from the nDSM varied depending on the filter size and the conditions of the applied filter. This suggests that conditional filtering is useful but sensitive to the conditions.

Flood delineation from synthetic aperture radar data with the help of a priori knowledge from historical acquisitions and digital elevation models in support of near-real-time flood mapping

Earth Resources and Environmental Remote Sensing/GIS Applications III, 2012

ABSTRACT The monitoring of flood events with synthetic aperture radar (SAR) sensors has attracted... more ABSTRACT The monitoring of flood events with synthetic aperture radar (SAR) sensors has attracted a considerable amount of attention during the last decade, owing to the growing interest in using spaceborne data in near-real time flood management. Most existing methods for classifying flood extent from SAR data rely on pure image processing techniques. In this paper, we propose a method involving a priori knowledge about an area taken from a multitemporal time series and a digital elevation model. A time series consisting of ENVISAT ASAR acquisitions was geocoded and coregistered. Then, a harmonic model was fitted to each pixel time series. The standardised residuals of the model were classified as flooded when exceeding a certain threshold value. Additionally, the classified flood extent was limited to flood-prone areas which were derived from a freely available DEM using the height above nearest drainage (HAND) index. Comparison with two different reference datasets for two different flood events showed that the approach yielded realistic results but underestimated the inundation extent. Among the possible reasons for this are the rather coarse resolution of 150 m and the sparse data coverage for a substantial part of the time series. Nevertheless, the study shows the potential for production of rapid overviews in near-real time in support of early response to flood crises.

International Journal of Applied Earth Observation and Geoinformation, 2015

Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in re... more Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in recent years. Most available algorithms typically focus on single-image techniques which do not take into account the backscatter signature of a land surface under non-flooded conditions. In this study, harmonic analysis of a multi-temporal time series of > 500 ENVISAT Advanced SAR (ASAR) scenes with a spatial resolution of 150 m is used to characterise the seasonality in backscatter under non-flooded conditions. Pixels which were inundated during a large-scale flood event during the summer 2007 floods of the River Severn (United Kingdom) showed strong deviations from normal seasonal behaviour as inferred from the harmonic model. The residuals were classified by means of an automatic threshold optimisation algorithm after masking out areas which are unlikely to be flooded using a topography-derived index. The results were validated against a reference dataset derived from high-resolution airborne imagery. For the water class, accuracies > 80 % were found for non-urban land uses. A slight underestimation of the reference flood extent can be seen, mostly due to the lower spatial resolution of the ASAR imagery. Finally, an outlook for the proposed algorithm is given in the light of the Sentinel-1 mission.

Remote Sensing, 2011

Roughness is an important input parameter for modeling of natural hazards such as floods, rock fa... more Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept (i.e., ranging from fine-scale soil characteristics to description of understory and lower tree layer) various roughness raster products were derived from the original full-waveform airborne laser scanning (FWF-ALS) point cloud using two different types of roughness parameters, the surface roughness (SR) and the terrain roughness (TR). For the calculation of the SR, ALS terrain points within a defined height range to the terrain surface are considered. For the parameterization of the SR, two approaches are investigated. In the first approach, a geometric description by calculating the standard deviation of plane fitting residuals of terrain points is used. In the second one, the potential of the derived echo widths are analyzed for the parameterization of SR. The echo width is an indicator for roughness and the slope of the target. To achieve a comparable spatial resolution of both SR layers, the calculation of the standard deviation of detrended terrain points requires a higher terrain point density than the SR parameterization using the echo widths. The TR describes objects (i.e., point clusters) close but explicitly above the terrain surface, with 20 cm defined as threshold height value for delineation of the surface layer (i.e., forest OPEN ACCESS Remote Sens. 2011, 3 504 floor layer). Two different empirically defined vegetation layers below the canopy layer were analyzed (TR I: 0.2 m to 1.0 m; TR II: 0.2 m to 3.0 m). A 1 m output grid cell size was chosen for all roughness parameters in order to provide consistency for further integration of high-resolution optical imagery. The derived roughness parameters were then jointly classified, together with a normalized Digital Surface Model (nDSM) showing the height of objects (i.e., trees) above ground. The presented approach enables the classification of forested areas in patches of different vegetation structure (e.g., varying soil roughness, understory, density of natural cover). For validation purposes in situ reference data were collected and cross-checked with the classification results, positively confirming the general feasibility of the proposed vertical concept of integrated roughness mapping on various vertical levels. Results can provide valuable input for forest mapping and monitoring, in particular with regard to natural hazard modeling.

Managing Forest Ecosystems, 2013

Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data a... more Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data acquisition around the year 2004. These systems typically digitize the analogue backscattered echo of the emitted laser pulse with a high frequency. FWF digitization has the advantage of not limiting the number of echoes that are recorded for each individual emitted laser pulse. Studies utilizing FWF data have shown that more echoes are provided from reflections in the vegetation in comparison to discrete echo systems. To obtain geophysical metrics based on ALS data that are independent of a mission's flying height, acquisition time or sensor characteristics, the FWF amplitude values can be calibrated, which is an important requirement before using them in further classification tasks. Beyond that, waveform digitization provides an additional observable which can be exploited in forestry, namely the width of the backscattered pulse (i.e. echo width). An early application of FWF ALS was to improve ground and shrub echo identification below the forest canopy for the improvement of terrain modelling, which can be achieved using the discriminative capability of the amplitude and echo width in classification algorithms. Further studies indicate that accuracies can be increased for classification (e.g. species) and biophysical parameter extraction (e.g. diameter at breast height) for single-tree-and area-based methods by exploiting the FWF observables amplitude and echo width.

Forest Delineation Based on Airborne LIDAR Data

Remote Sensing, 2012

Sensors (Basel, Switzerland), 2011

In this study, a semi-empirical model that was originally developed for stem volume estimation is... more 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 be measured 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...

Österreichische Wasser- und Abfallwirtschaft, 2009

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

Individual tree crowns can be delineated from dense airborne laser scanning (ALS) data and their ... more 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.

Laser Radar Technology and Applications XII, 2007

Direct detection laser radar systems with echo signal digitization and subsequent full waveform a... more Direct detection laser radar systems with echo signal digitization and subsequent full waveform analysis provide additional information on the target's properties compared to conventional discrete echo systems. We focus on the advantages of utilizing the additional information especially in the course of airborne laser scanning, improving for example the mandatory process for classifying the measurement data for generating high-quality digital

The NEWFOR single tree detection benchmark–A test of LIDAR based detection methods using a unique dataset of different forest types within the alpine space