scholarly journals A Robust SAR Speckle Tracking Workflow for Measuring and Interpreting the 3D Surface Displacement of Landslides

2021 ◽  
Vol 13 (15) ◽  
pp. 3048
Author(s):  
Davide Donati ◽  
Bernhard Rabus ◽  
Jeanine Engelbrecht ◽  
Doug Stead ◽  
John Clague ◽  
...  
Keyword(s):  
Speckle Tracking ◽  
Laser Scanning ◽  
Surface Displacement ◽  
Post Processing ◽  
Remotely Sensed Data ◽  
Slide Mass ◽  
Site Investigations ◽  
Displacement Vectors ◽  
Slow Moving ◽  
Gis Environment

We present a workflow for investigating large, slow-moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post-processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five-year windows, 2010–2015 and 2015–2020. Then, we perform post-processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST-derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations.

scholarly journals Distribution Statistics Preserving Post-Processing Method With Plot Level Uncertainty Analysis for Remotely Sensed Data-Based Forest Inventory Predictions

2018 ◽  
Vol 10 (11) ◽  
pp. 1677
Author(s):  
Virpi Junttila ◽  
Tuomo Kauranne
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Linear Models ◽  
Accuracy Assessment ◽  
Remotely Sensed ◽  
Processing Method ◽  
Post Processing ◽  
Remotely Sensed Data ◽  
Training Set ◽  
Study Sites

Remotely sensed data-based models used in operational forest inventory usually give precise and accurate predictions on average, but they often suffer from systematic under- or over-estimation of extreme attribute values resulting in too narrow or skewed attribute distributions. We use a post-processing method based on the statistics of a proper, representative training set to correct the predictions and their probability intervals, attaining corrected predictions that reproduce the statistics of the whole population. Performance of the method is validated with three forest attributes from seven study sites in Finland with training set sizes from 50 to over 400 field plots. The results are compared to those of the uncorrected predictions given by linear models using airborne laser scanning data. The post-processing method improves the accuracy assessment linear fit between the predictions and the reference set by 35.4–51.8% and the distribution fit by 44.5–95.0%. The prediction root mean square error declines on the average by 6.3%. The systematic under- and over-estimation are reduced consistently with all training set sizes. The level of uncertainty is maintained well as the probability intervals cover the real uncertainty while keeping the average probability interval width similar to the one in uncorrected predictions.

Monitoring analysis and environmental assessment of mine subsidence based on surface displacement monitoring

2021 ◽  
pp. 1-12
Author(s):  
Kuo Ding ◽  
Hui Li
Keyword(s):  
Surface Deformation ◽  
Surface Displacement ◽  
Horizontal Stress ◽  
Structural Features ◽  
Displacement Curve ◽  
Ground Fissures ◽  
Metal Mine ◽  
Displacement Vectors ◽  
Dependent Behavior ◽  
Subsidence Zone

Over the past several years, a metal mine by block caving method has experienced a long-term and progressive surface deformation and fracturing, and then we start our investigation based on this background. The location of surface rupture was based on a series of mapping activities and the deformation data was collected by GPS from 2013 to 2016. In this paper, emphasis was put on the analysis of the fissures, deformation and stress of surface subsidence. Results reveal the diversity magnitude and structural features of surface deformation and ground fissures. In addition, the time dependent behavior is comprehended and the subsidence zone reflects different types of time-displacement curve – regressive phase, steady phase and progressive phase, all these achievements indicate the complexity and diversity of the subsidence zone. On the other hand, stress calculation which inspired from the mechanical model of the cracking of hole wall is carried out, it is meaningful to understand the relation between fracture features, displacement vectors and horizontal stress.

scholarly journals Denoising of Laser Scanning Data Using Wavelet

2014 ◽  
Vol 12 ◽  
pp. 41-47 ◽  
Author(s):  
Petr Jašek ◽  
Martin Štroner
Keyword(s):  
Image Processing ◽  
Laser Scanning ◽  
Wavelet Transformation ◽  
Terrestrial Laser Scanning ◽  
Range Image ◽  
Regular Matrix ◽  
Post Processing ◽  
Spatial Coordinates ◽  
Measured Distance ◽  
Made In

Regarding the terrestrial laser scanning accuracy, one of the main problems is the noise in measured distance which is necessary for the spatial coordinates´ determination. In this paper the technique of using the wavelet transformation for the reduction of the noise in the laser scanning data is described. This method of filtration is made in “post processing” and due to this fact any changes in the measuring procedure in the field shouldn´t be done. The creation of the regular matrix is needed to apply image processing. This matrix then makes the range image. In the paper real and simulated efficiency tests of wavelet transformation, the final summary and advantages or disadvantages of this method are introduced.

A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties

2019 ◽  
Vol 93 (1) ◽  
pp. 150-162 ◽  
Author(s):  
Stefano Puliti ◽  
Jonathan P Dash ◽  
Michael S Watt ◽  
Johannes Breidenbach ◽  
Grant D Pearse
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Stem Volume ◽  
Stem Density ◽  
Multiple Sources ◽  
Remotely Sensed Data ◽  
Biophysical Parameters ◽  
Auxiliary Data ◽  
Airborne Laser ◽  
Small Forest

Abstract This study addresses the use of multiple sources of auxiliary data from unmanned aerial vehicles (UAVs) and airborne laser scanning (ALS) data for inference on key biophysical parameters in small forest properties (5–300 ha). We compared the precision of the estimates using plot data alone under a design-based inference with model-based estimates that include plot data and the following four types of auxiliary data: (1) terrain-independent variables from UAV photogrammetric data (UAV-SfM); (2) variables obtained from UAV photogrammetric data normalized using external terrain data (UAV-SfMDTM); (3) UAV-LS and (4) ALS data. The inclusion of remotely sensed data increased the precision of DB estimates by factors of 1.5–2.2. The optimal data sources for top height, stem density, basal area and total stem volume were: UAV-LS, UAV-SfM, UAV-SfMDTM and UAV-SfMDTM. We conclude that the use of UAV data can increase the precision of stand-level estimates even under intensive field sampling conditions.

scholarly journals Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement

2020 ◽  
Vol 12 (8) ◽  
pp. 1236 ◽  
Author(s):  
Karel Kuželka ◽  
Martin Slavík ◽  
Peter Surový
Keyword(s):  
Cross Sections ◽  
Hough Transform ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Bias Error ◽  
Bias Error ◽  
Least Trimmed Squares ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Tree Stem

Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.

Detection and measurement of landslide deformation prior to their failure by satellite radar interferometry.

2020 ◽  
Author(s):  
Alessandro Simoni ◽  
Benedikt Bayer ◽  
Pierpaolo Ciuffi ◽  
Silvia Franceschini ◽  
Matteo Berti
Keyword(s):  
Surface Displacement ◽  
Radar Interferometry ◽  
Rock Outcrops ◽  
Bare Rock ◽  
Rainfall Events ◽  
Surface Displacements ◽  
Catastrophic Failures ◽  
Before And After ◽  
Slow Moving ◽  
Satellite Radar

<p>Landslides are widespread landscape features in the Northern Apennine mountain chain and their activity frequently cause damages to settlements and infrastructures. In such context, slow-moving landslides are very common and typically affect fine-grained weathered rocks. Long periods of sustained slow-movements (cms/year) can be interrupted by rapid acceleration and catastrophic failures (ms/day) that are caused by intense rainfall events. Space-borne synthetic aperture radar interferometry (InSAR) proved effective to detect actively deforming phenomena and monitor their evolution in the periods before and after failures. We present InSAR results derived from the Sentinel 1 satellite constellation for landslide cases that underwent reactivation during 2019. In all cases, the catastrophic failures were unexpected and no ground-based monitoring data are available. We processed pre- and post-failure interferograms of SAR images acquired by Sentinel 1 A/B with time spans ranging from 6 to 24 days, removing those having low coherence by manual inspection. The conventional 2-pass technique allowed us to obtain measurements of surface displacement despite the fact that sparse to none infrastructures nor bare rock outcrops are present on the landslide bodies. Our interferograms show that surface displacements are visible well in advance of the actual failure. They display nearly continuous downslope motion with seasonal velocity changes. Time series between 2015 and 2019 shows that surface displacements can be appreciated throughout most part of the year with snow cover and summer peak of vegetation being the most notable exceptions. Distinct accelerations can be detected in space and time during the weeks and months preceding the reactivation.</p><p>We compare time-dependent deformations to precipitation patterns to explore their relationship and to document the transition from stable to unstable deformation. Our work suggests that InSAR interferometry can be successfully used to measure pre-failure displacements and detect slow-moving landslides that are more prone to reactivation in case of rainfall events.</p>

scholarly journals VIRTUAL REALITY FOR HISTORICAL ARCHITECTURE

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 9-16 ◽  
Author(s):  
F. Agnello ◽  
F. Avella ◽  
S. Agnello
Keyword(s):  
Virtual Reality ◽  
Cultural Heritage ◽  
Laser Scanning ◽  
3D Model ◽  
Post Processing ◽  
Virtual Navigation ◽  
Virtual Tour ◽  
Current State ◽  
Digital Cultural Heritage ◽  
Historical Architecture

<p><strong>Abstract.</strong> This article shows a first step in the development of an immersive virtual tour of the Cathedral of Palermo, entering the fields of Digital Cultural Heritage and Edutainment. Its purpose is to help people to gain knowledge about the site, highlighting the complex stratifications that have characterized its history.</p><p>The development of the project has been possible thanks to different phases of work: surveys were initially carried out by laser scanning, then assembled and processed to obtain the 3D model of the current state; at the same time, the model of reconstruction was processed in several phases, based on historical, archival and iconographic sources; both models were, later, subject to post-processing, preparatory to the development of virtual navigation. The tour scenario includes options in order to make it attractive for the player, such as interactive elements, interfaces and animations.</p>

scholarly journals Graph-Based Classification and Urban Modeling of Laser Scanning and Imagery: Toward 3D Smart Web Services

2021 ◽  
Vol 14 (1) ◽  
pp. 114
Author(s):  
Slim Namouchi ◽  
Imed Riadh Farah
Keyword(s):  
Geometric Modeling ◽  
Laser Scanning ◽  
Ground Surface ◽  
Urban Environments ◽  
Urban Modeling ◽  
Military Operations ◽  
Remotely Sensed Data ◽  
Reconstruction Process ◽  
Interchange Format ◽  
Smart Service

Recently, remotely sensed data obtained via laser technology has gained great importance due to its wide use in several fields, especially in 3D urban modeling. In fact, 3D city models in urban environments are efficiently employed in many fields, such as military operations, emergency management, building and height mapping, cadastral data upgrading, monitoring of changes as well as virtual reality. These applications are essentially composed of models of structures, urban elements, ground surface and vegetation. This paper presents a workflow for modeling the structure of buildings by using laser-scanned data (LiDAR) and multi-spectral images in order to develop a 3D web service for a smart city concept. Optical vertical photography is generally utilized to extract building class, while LiDAR data is used as a source of information to create the structure of the 3D building. The building reconstruction process presented in this study can be divided into four main stages: building LiDAR points extraction, piecewise horizontal roof clustering, boundaries extraction and 3D geometric modeling. Finally, an architecture for a 3D smart service based on the CityGML interchange format is proposed.

scholarly journals The Petawawa Research Forest: Establishment of a remote sensing supersite

2019 ◽  
Vol 95 (03) ◽  
pp. 149-156 ◽  
Author(s):  
Joanne C. White ◽  
Hao Chen ◽  
Murray E. Woods ◽  
Brian Low ◽  
Sasha Nasonova
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
New Technologies ◽  
Open Data ◽  
Digital Terrain Model ◽  
Lessons Learned ◽  
Remotely Sensed Data ◽  
The Public ◽  
Terrain Model ◽  
Digital Terrain

The pace of technological change in forest inventory and monitoring over the past 50 years has been remarkable, largely asa result of the increased availability of various forms of remotely sensed data. Benchmarking sites, with the requisite refer-ence and baseline data for evaluating the capacities of new technologies, algorithms, and approaches, can be extremely valu-able for sparking innovation, as well as for enabling transparent and scientifically sound assessments of technologies, newdata streams, and associated information outcomes. Herein we describe the establishment of a remote sensing supersite atthe Petawawa Research Forest (PRF) in southern Ontario, Canada, and summarize the open access datasets that have beencompiled and made available to the public. The PRF is approximately 10 000 ha in size and represents a complex assemblageof tree species and forest structures. More than 1900 data records, including multiple airborne laser scanning datasets andassociated derivatives (i.e., digital terrain model, canopy height model), airborne imagery, satellite remote sensing timeseries, and ground plot data, among others, have been made openly available for download from Canada’s National ForestInformation System. We identify issues and present opportunities associated with the establishment of a remote sensingsupersite at the PRF, as well as share some of the lessons learned to foster the establishment and open data sharing for othernational and international remote sensing supersites. The PRF supersite can be accessed from the following link: https://opendata.nfis.org/mapserver/PRF.html .

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