Sedimentological characterization of Antarctic moraines using UAVs and Structure-from-Motion photogrammetry

In glacial environments particle-size analysis of moraines provides insights into clast origin, transport history, depositional mechanism and processes of reworking. Traditional methods for grain-size classification are labour-intensive, physically intrusive and are limited to patch-scale (1m2) observation. We develop emerging, high-resolution ground- and unmanned aerial vehicle-based ‘Structure-from-Motion’ (UAV-SfM) photogrammetry to recover grain-size information across an moraine surface in the Heritage Range, Antarctica. SfM data products were benchmarked against equivalent datasets acquired using terrestrial laser scanning, and were found to be accurate to within 1.7 and 50mm for patch- and site-scale modelling, respectively. Grain-size distributions were obtained through digital grain classification, or ‘photo-sieving’, of patch-scale SfM orthoimagery. Photo-sieved distributions were accurate to <2mm compared to control distributions derived from dry sieving. A relationship between patch-scale median grain size and the standard deviation of local surface elevations was applied to a site-scale UAV-SfM model to facilitate upscaling and the production of a spatially continuous map of the median grain size across a 0.3 km2 area of moraine. This highly automated workflow for site scale sedimentological characterization eliminates much of the subjectivity associated with traditional methods and forms a sound basis for subsequent glaciological process interpretation and analysis

Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Multi-technique approach to rockfall monitoring in the Montserrat massif (Catalonia, NE Spain)

Montserrat Mountain is located near Barcelona in Catalonia, in the northeast of Spain, and its massif is formed by conglomerate interleaved by siltstone/sandstone with steep slopes very prone to rockfalls. The increasing number of visitors in the monastery area, reaching 2.4 million per year, has highlighted the risk derived from rockfalls for this building area and also for the terrestrial accesses, both roads and the rack railway. A risk mitigation plan has been launched, and its first phase during 2014-2016 has been focused largely on testing several monitoring techniques for their later implementation. The results of the pilot tests, performed as a development from previous sparse experiences and data, are presented together with the first insights obtained. These tests combine four monitoring techniques under different conditions of continuity in space and time domains, which are: displacement monitoring with Ground-based Synthetic Aperture Radar and characterization at slope scale, with an extremely non-uniform atmospheric phase screen due to the stepped topography and atmosphere stratification; Terrestrial Laser Scanner surveys quantifying the frequency of small or even previously unnoticed rockfalls, and monitoring rock block centimetre scale displacements; the monitoring of rock joints implemented through a wireless sensor network with an ad hoc design of ZigBee loggers developed by ICGC; and, finally, monitoring singular rock needles with Total Station.Peer ReviewedPostprint (author's final draft

Computational virtual measurement for trees

National forest inventory (NFI) is a systematic sampling method to collect forest information, including tree parameters, site conditions, and auxiliary data. The sample plot measurement is the key work in NFI. However, compared to the techniques 100 years ago, measuring methods and data-processing (modeling) approaches for NFI sample plots have been improved to a minor extent. The limit was that the newly-developed methods introduced additional validation workflows and would increase the workload in NFI. That was due to that these methods were usually developed based on species-specific and site-specific strategies. In order to overcome these obstacles, the integration of the novel measuring instruments is in urgent need, e.g., light detection and ranging (LiDAR) and the corresponding data processing methods with NFI. Given these situations, this thesis proposed a novel computational virtual measurement (CVM) method for the determination of tree parameters without the need for validation. Primarily, CVM is a physical simulation method and works as a virtual measuring instrument. CVM measures raw data, e.g., LiDAR point clouds and tree models, by the simulation of the physical mechanism of measuring instruments and natural phenomena. Based on the theory of CVM, this thesis is a systematic description of how to develop virtual measuring instruments. The first work is to introduce the CVM theory. CVM is a conceptual and general methodology, which is different from a specific measurement of tree parameters. Then, the feasibility of CVM was tested using a conceptual implementation, i.e., virtual ruler. The development of virtual ruler demonstrated the two key differences between CVM and conventional modeling methods. Firstly, the research focus of CVM is to build an appropriate physical scenario instead of finding a mathematical relationship between modeling results and true values. Secondly, the CVM outputs can approach true values, whereas the modeling results could not. Consequently, in a virtual space, tree parameters are determined by a measuring process without mathematical predictions. Accordingly, the result is free of validation and can be regarded as true values, at least in virtual spaces. With the knowledge from the virtual ruler development, two exceptional implementations are further developed. They are the virtual water displacement (VWD) method and sunlight analysis method. Both of them employ the same CVM workflow, which is firstly measured in reality and secondly measured in virtual space. The VWD aims to virtually measure the point clouds using the simulation of water displacement methods in reality. There are two stages in this method. The first stage is to apply the simulation of water displacement using massive virtual water molecules (VWMs). Some empirical regressions have to be employed in this stage, due to the limitation of computer performance. In the second stage, a single (or few) VWM (or VWMs) is developed to remove those empirical processes in VWD. Finally, VWD can function as a fully automatic method to measure point clouds.The sunlight analysis method aims to virtually measure the tree models using the simulation of solar illumination during daylight. There are also two stages in this method. The first stage is to develop sunlight analysis for a single tree. The second stage is to analyze the interference from neighboring trees. The results include default tree attributes, which can be collected in the future NFI. The successful developments of CVM, along with implementations of VWD and sunlight analysis methods, prove the initial assumptions in this thesis. It is the conversion of mathematical processing of data into virtual measurements. Accordingly, this is a different philosophy, i.e., the role of data is extended to the digital representative of trees. It opens an avenue of data processing using a more natural approach and is expected to be employed in the near future as a standard measuring instrument, such as a diameter tape, in NFI.Die Nationale Waldinventur (NFI) ist eine systematische Stichprobenmethode zur Erfassung von Waldinformationen, einschließlich Baumparameter, Standortbedingungen und Hilfsdaten. Die Messung von Stichprobenparzellen ist die Schlüsselarbeit der NFI. Im Vergleich zu den Techniken vor 100 Jahren wurden die Messmethoden und Datenverarbeitungsansätze (Modellierung) für NFI-Stichprobenparzellen jedoch in geringem Umfang verbessert. Die Grenze lag darin, dass die neu entwickelten Methoden zusätzliche Validierungsabläufe einführten und den Arbeitsaufwand in der NFI erhöhen würden. Dies war darauf zurückzuführen, dass diese Methoden in der Regel auf der Grundlage art- und standortspezifischer Strategien entwickelt wurden. Um diese Hindernisse zu überwinden, ist die Integration der neuartigen Messinstrumente dringend erforderlich, z.B. Light Detection and Ranging (LiDAR) und die entsprechenden Datenverarbeitungsmethoden mit NFI. Vor diesem Hintergrund wird in dieser Arbeit ein neuartiges rechnergestütztes virtuelles Messverfahren (CVM) zur Bestimmung von Baumparametern ohne Validierungsbedarf vorgeschlagen. CVM ist in erster Linie eine physikalische Simulationsmethode und arbeitet als virtuelles Messinstrument. CVM misst Rohdaten, z.B. LiDAR-Punktwolken und Baummodelle, durch die Simulation des physikalischen Mechanismus von Messinstrumenten und Naturphänomenen. Basierend auf der Theorie des CVM ist diese Arbeit eine systematische Beschreibung, wie virtuelle Messinstrumente entwickelt werden können. Die erste Arbeit dient der Einführung in die Theorie des CVM. CVM ist eine konzeptuelle und allgemeine Methodik, die sich von einer spezifischen Messung von Baumparametern unterscheidet. Anschliessend wird die Durchführbarkeit des CVM anhand einer konzeptuellen Implementierung, d.h. eines virtuellen Lineals, getestet. Die Entwicklung des virtuellen Lineals zeigte die beiden Hauptunterschiede zwischen CVM und konventionellen Modellierungsmethoden auf. Erstens besteht der Forschungsschwerpunkt von CVM darin, ein geeignetes physisches Szenario zu erstellen, anstatt eine mathematische Beziehung zwischen Modellierungsergebnissen und wahren Werten zu finden. Zweitens können sich die Ergebnisse des CVM den wahren Werten annähern, während die Modellierungsergebnisse dies nicht konnten. Folglich werden in einem virtuellen Raum die Baumparameter durch einen Messprozess ohne mathematische Vorhersagen bestimmt. Dementsprechend ist das Ergebnis frei von Validierung und kann, zumindest in virtuellen Räumen, als wahre Werte betrachtet werden. Mit dem Wissen aus der Entwicklung des virtuellen Lineals werden zwei aussergewöhnliche Implementierungen weiterentwickelt. Es handelt sich um die Methode der virtuellen Wasserverdrängung (VWD) und die Methode der Sonnenlichtanalyse. Beide verwenden den gleichen CVM-Workflow, der erstens in der Realität und zweitens im virtuellen Raum gemessen wird. Das VWD zielt darauf ab, die Punktwolken virtuell zu messen, wobei die Simulation von Wasserverdrängungsmethoden in der Realität verwendet wird. Diese Methode besteht aus zwei Stufen. Die erste Stufe besteht in der Anwendung der Simulation der Wasserverdrängung unter Verwendung massiver virtueller Wassermoleküle (VWMs). Aufgrund der begrenzten Computerleistung müssen in dieser Phase einige empirische Regressionen angewandt werden. In der zweiten Stufe wird ein einzelnes (oder wenige) VWM (oder VWMs) entwickelt, um diese empirischen Prozesse im VWD zu entfernen. Schließlich kann VWD als vollautomatische Methode zur Messung von Punktwolken fungieren. Die Methode der Sonnenlichtanalyse zielt darauf ab, die Baummodelle virtuell zu messen, indem die Simulation der Sonneneinstrahlung bei Tageslicht verwendet wird. Auch bei dieser Methode gibt es zwei Stufen. In der ersten Stufe wird die Sonnenlichtanalyse für einen einzelnen Baum entwickelt. Die zweite Stufe ist die Analyse der Interferenz von benachbarten Bäumen. Die Ergebnisse umfassen Standard-Baumattribute, die in der zukünftigen NFI gesammelt werden können. Die erfolgreichen Entwicklungen von CVM, zusammen mit Implementierungen von VWD- und Sonnenlichtanalysemethoden, beweisen die anfänglichen Annahmen in dieser Arbeit. Es handelt sich um die Umsetzung der mathematischen Verarbeitung von Daten in virtuelle Messungen. Dementsprechend handelt es sich um eine andere Philosophie, d.h. die Rolle der Daten wird auf die digitale Darstellung von Bäumen ausgedehnt. Sie eröffnet einen Weg der Datenverarbeitung unter Verwendung eines natürlicheren Ansatzes und wird voraussichtlich in naher Zukunft als Standard-Messinstrument, wie z.B. ein Durchmesser-Band, in der NFI eingesetzt werden

Microgravity Particle Research on the Space Station

Science questions that could be addressed by a Space Station Microgravity Particle Research Facility for studying small suspended particles were discussed. Characteristics of such a facility were determined. Disciplines covered include astrophysics and the solar nebula, planetary science, atmospheric science, exobiology and life science, and physics and chemistry

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

Suitability of ground-based SfM-MVS for monitoring glacial and periglacial processes

Photo-based surface reconstruction is rapidly emerging as an alternative survey technique to lidar (light detection and ranging) in many fields of geoscience fostered by the recent development of computer vision algorithms such as structure from motion (SfM) and dense image matching such as multi-view stereo (MVS). The objectives of this work are to test the suitability of the ground-based SfM-MVS approach for calculating the geodetic mass balance of a 2.1km2 glacier and for detecting the surface displacement of a neighbouring active rock glacier located in the eastern Italian Alps. The photos were acquired in 2013 and 2014 using a digital consumer-grade camera during single-day field surveys. Airborne laser scanning (ALS, otherwise known as airborne lidar) data were used as benchmarks to estimate the accuracy of the photogrammetric digital elevation models (DEMs) and the reliability of the method. The SfM-MVS approach enabled the reconstruction of high-quality DEMs, which provided estimates of glacial and periglacial processes similar to those achievable using ALS. In stable bedrock areas outside the glacier, the mean and the standard deviation of the elevation difference between the SfM-MVS DEM and the ALS DEM was-0.42 \ub1 1.72 and 0.03 \ub1 0.74 m in 2013 and 2014, respectively. The overall pattern of elevation loss and gain on the glacier were similar with both methods, ranging between-5.53 and + 3.48 m. In the rock glacier area, the elevation difference between the SfM-MVS DEM and the ALS DEM was 0.02 \ub1 0.17 m. The SfM-MVS was able to reproduce the patterns and the magnitudes of displacement of the rock glacier observed by the ALS, ranging between 0.00 and 0.48 m per year. The use of natural targets as ground control points, the occurrence of shadowed and low-contrast areas, and in particular the suboptimal camera network geometry imposed by the morphology of the study area were the main factors affecting the accuracy of photogrammetric DEMs negatively. Technical improvements such as using an aerial platform and/or placing artificial targets could significantly improve the results but run the risk of being more demanding in terms of costs and logistics

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGBgrass r2 = 0.42, AGBtotal r2 = 0.32) than the TLS (AGBgrass r2 = 0.46, AGBtotal r2 = 0.57) or SfM (AGBgrass r2 = 0.54, AGBtotal r2 = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems

Stratospheric measurement requirements and satellite-borne remote sensing capabilities

The capabilities of specific NASA remote sensing systems to provide appropriate measurements of stratospheric parameters for potential user needs were assessed. This was used to evaluate the capabilities of the remote sensing systems to perform global monitoring of the stratosphere. The following conclusions were reached: (1) The performance of current remote stratospheric sensors, in some cases, compares quite well with identified measurement requirements. Their ability to measure other species has not been demonstrated. (2) None of the current, in-situ methods have the capability to satisfy the requirements for global monitoring and the temporal constraints derived from the users needs portion of the study. (3) Existing, non-remote techniques will continue to play an important role in stratospheric investigations for both corroboration of remotely collected data and in the evolutionary development of future remote sensors

Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment

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/m2