scholarly journals High-Resolution Spaceborne, Airborne and In Situ Landslide Kinematic Measurements of the Slumgullion Landslide in Southwest Colorado

2019 ◽  
Vol 11 (3) ◽  
pp. 265 ◽  
Author(s):  
Austin Madson ◽  
Eric Fielding ◽  
Yongwei Sheng ◽  
Kyle Cavanaugh
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Measurement Techniques ◽  
Remotely Sensed ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Motion Vectors ◽  
Active Portion

The Slumgullion landslide, located in southwestern Colorado, has been active since the early 1700s and current data suggests that the most active portion of the slide creeps at a rate of ~1.5–2.0 cm/day. Accurate deformation measurement techniques are vital to the understanding of persistent, yet slow-moving landslides like the Slumgullion. The factors that affect slope movements at the Slumgullion are on-time scales that are well suited towards a remotely sensed approach to constrain the 12 different kinematic units that make up the persistent creeping landslide. We derive a time series of motion vectors (magnitude and direction) using subpixel offset techniques from very high resolution TerraSAR-X Staring Spotlight ascending/descending data as well as from a novel high-resolution amalgamation of airborne lidar and unmanned aerial systems (UAS) Structure from Motion (SfM) digital surface model (DSM) hillshades. Deformation rates calculated from the spaceborne and airborne datasets show high agreement (mean difference of ~0.9 mm/day), further highlighting the potential for the monitoring of ongoing mass wasting events utilizing unmanned aircraft systems (UAS) We compare pixel offset results from an 11-day synthetic aperture radar (SAR) pair acquired in July of 2016 with motion vectors from a coincident low-cost L1 only Global Navigation Satellite System (GNSS) field campaign in order to verify the remotely sensed results and to derive the accuracy of the azimuth and range offsets. We find that the average azimuth and range pixel offset accuracies utilizing the methods herein are on the order of 1/18 and 1/20 of their along-track and slant range focused ground pixel spacing values of 16.8 cm and 45.5 cm, respectively. We utilize the SAR offset time series to add a twelfth kinematic unit to the previously established set of eleven unique regions at the site of an established minislide within the main landslide itself. Lastly, we compare the calculated rates and direction from all spaceborne- and airborne-derived motion vectors for each of the established kinematic zones within the active portion of the landslide. These comparisons show an overall increased magnitude and across-track component (i.e., more westerly angles of motion) for the descending SAR data as compared to their ascending counterparts. The processing techniques and subsequent results herein provide for an improved knowledge of the Slumgullion landslide’s kinematics and this increased knowledge has implications for the advancement of measurement techniques and the understanding of globally distributed creeping landslides.

scholarly journals Estimating mass-wasting processes in active earth slides – earth flows with time-series of High-Resolution DEMs from photogrammetry and airborne LiDAR

2009 ◽  
Vol 9 (2) ◽  
pp. 433-439 ◽  
Author(s):  
A. Corsini ◽  
L. Borgatti ◽  
F. Cervi ◽  
A. Dahne ◽  
F. Ronchetti ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Large Scale ◽  
Airborne Lidar ◽  
Mitigation Measures ◽  
Mass Wasting ◽  
Aerial Photos ◽  
Use Of Time ◽  
Elevation Changes ◽  
Earth Flows

Abstract. This paper deals with the use of time-series of High-Resolution Digital Elevation Models (HR DEMs) obtained from photogrammetry and airborne LiDAR coupled with aerial photos, to analyse the magnitude of recently reactivated large scale earth slides – earth flows located in the northern Apennines of Italy. The landslides underwent complete reactivation between 2001 and 2006, causing civil protection emergencies. With the final aim to support hazard assessment and the planning of mitigation measures, high-resolution DEMs are used to identify, quantify and visualize depletion and accumulation in the slope resulting from the reactivation of the mass movements. This information allows to quantify mass wasting, i.e. the amount of landslide material that is wasted during reactivation events due to stream erosion along the slope and at its bottom, resulting in sediment discharge into the local fluvial system, and to assess the total volumetric magnitude of the events. By quantifying and visualising elevation changes at the slope scale, results are also a valuable support for the comprehension of geomorphological processes acting behind the evolution of the analysed landslides.

scholarly journals Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling

2021 ◽  
Author(s):  
S. Blundell
Keyword(s):  
High Resolution ◽  
Route Planning ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Added Value ◽  
Surface Model ◽  
Mobility Modeling ◽  
Sparse Vegetation ◽  
Elevation Model ◽  
Terrain Features

Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolution elevation models of canopy. The identification and mapping of canopy cover and micro-terrain features in areas of sparse vegetation is demonstrated with an elevation model for a region of western Montana, using algorithms for breaklines, elevation differencing, slope, terrain ruggedness, and breakline gradient direction. These algorithms were created at the U.S. Army Engineer Research Center – Geospatial Research Laboratory (ERDC-GRL) and can be accessed through an in-house tool constructed in the ENVI/IDL environment. After breakline processing, products from these algorithms are brought into a Geographic Information System as analytical layers and applied to a mobility routing model, demonstrating the effect of breaklines as obstacles in the calculation of optimal, off-road routes. Elevation model breakline analysis can serve as significant added value to micro-terrain feature and canopy mapping, obstacle identification, and route planning.

scholarly journals On the Reliable Generation of 3D City Models from Open Data

Urban Science ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 47
Author(s):  
Renoy Girindran ◽  
Doreen S Boyd ◽  
Julian Rosser ◽  
Dhanya Vijayan ◽  
Gavin Long ◽  
...  
Keyword(s):  
High Resolution ◽  
Open Source ◽  
3D Model ◽  
Airborne Lidar ◽  
Data Availability ◽  
Small Subset ◽  
Surface Model ◽  
High Resolution Data ◽  
3D City Models ◽  
City Models

A 3D model communicates more effectively than a 2D model, hence the applications of 3D city models are rapidly gaining significance in urban studies. However, presently, there is a dearth of free of cost, high-resolution 3D city models available for use. This paper offers potential solutions to this problem by providing a globally replicable methodology to generate low-cost 3D city models from open source 2D building data in conjunction with open satellite-based elevation datasets. Two geographically and morphologically different case studies were used to develop and test this methodology: the Chinese city of Shanghai and the city of Nottingham in the UK. The method is based principally on OpenStreetMap (OSM) and Advanced Land Observing Satellite World 3D digital surface model (AW3D DSM) data and use GMTED 2010 DTM data for undulating terrain. Further enhancement of the resultant 3D model, though not compulsory, uses higher resolution elevation models that are not always open source, but if available can be used (i.e., airborne LiDAR generated DTM). Further we test and develop methods to improve the accuracy of the generated 3D models, employing a small subset of high resolution data that are not open source but can be purchased with a minimal budgets. Given these scenarios of data availability are globally applicable and time-efficient for 3D building generation (where 2D building footprints are available), our proposed methodology has the potential to accelerate the production of 3D city models, and thus to facilitate their dependent applications (e.g., disaster management) wherever commercial 3D city models are unavailable.

scholarly journals VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 675-682
Author(s):  
D. James ◽  
A. Collin ◽  
A. Mury ◽  
S. Costa
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Large Scale ◽  
Near Infrared ◽  
Airborne Lidar ◽  
Support Vector ◽  
Surface Model ◽  
Global Changes ◽  
Very High

Abstract. Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km2, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %).

Monitoring of a landslide through the use of UAV survey

2020 ◽  
Author(s):  
Simone Pillon ◽  
Davide Martinucci ◽  
Annelore Bezzi ◽  
Giulia Casagrande ◽  
Giorgio Fontolan ◽  
...  
Keyword(s):  
High Resolution ◽  
Control Point ◽  
Ground Control ◽  
Surface Model ◽  
Ground Control Point ◽  
North East ◽  
Active Portion ◽  
High Resolution Images ◽  
Precise Assessment ◽  
Friuli Venezia Giulia

<p>The monitoring of landslides using UAVs is particularly convenient as these are dangerous areas that present access difficulties. This study aims to integrate monitoring carried out via traditional techniques (GNSS and total station surveys of benchmarks) with UAV photogrammetric survey, as the latter allows for a precise assessment of the volumes affected by movement. The Masarach landslide, located in Friuli Venezia Giulia (north east Italy), covers an area of approximately 200 ha. Two surveys were carried out two years apart in order to measure displacements of much greater magnitude than instrumental errors. In the first survey, restricted to the most active area, a six rotor UAV was used, with a maximum take-off mass of 4 kg, which carried a 20 Mpixel APS-C camera. 243 high resolution images were captured and 27 GCPs (Ground Control Point) were surveyed with a GNSS RTK reciever. In the second survey a DJI Phantom 4 Pro UAV was used, carrying a 20 Mpixel 1“ sensor camera. 978 high resolution images were captured and 40 GCPs (Ground Control Point) were surveyed with a GNSS RTK reciever. Data were analyzed using Agisoft Metashape Professional to produce an orthophoto and a DSM (Digital Surface Model) with a ground resolution of 0.02 m and 0.04 m respectively. The DSMs were compared in ArcGIS to calculate the moving masses and highlight the areas of greatest instability. It emerged that approximately 10,000 cubic meters of landslide material were transported to the Arzino stream below, with verified displacements on the control point ranging from meters to centimeters. This work made it possible to accurately define the most active portion of the landslide.</p>

Progressive landslide activity analysis and monitoring from Multi-temporal high-resolution geoinfomatic data sets

2020 ◽  
Author(s):  
Kuo-Jen Chang ◽  
Chih-Ming Tseng ◽  
Ho-Hsuan Chang ◽  
Mei-Jen Huang
Keyword(s):  
High Resolution ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Landslide Monitoring ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Hazardous Area ◽  
Multi Temporal ◽  
Rtk Gps

<p>Due to the high seismicity and high annual precipitation, numerous landslides have occurred and caused severe impact in Taiwan. In recent years, the remote sensing technology improves rapidly, providing a wide range of image, essential and precise geoinformation. The Small unmanned aircraft system (sUAS) has been widely used in landslide monitoring and geomorphic change detection. To access potential hazards we combine sUAS, field survey, terrestrial laser scanner (ground LiDAR) and UAS LiDAR for data acquisition. Based on the methods we construct multi-temporal high-resolution DTMs so as to access the activity and to monitoring the creeping landslides in Paolai village, southern Taiwan. The data set are qualified from 21 ground control points (GCPs) and 11 check points (CPs) based on real-time kinematic-global positioning system (RTK-GPS) and VBS RTK-GPS (e-GNSS). Since 2015, more than 10 geospatial datasets have been produced for an area between 5-80 Km<sup>2</sup> with 8-12 cm spatial resolution. These datasets were then compared with the airborne LiDAR data to access the quality and interpretability of the data sets. Since 2017, we integrate UAS LiDAR to monitoring landslide area, and re-evaluate the data accuracy. Since 2018 we have integrate UAS LiDAR, terrestrial LiDAR, and photogrammetric point cloud for landslide study, to ensure no shadow effect of the dataset. The geomorphologic changes and landslide activities were quantified in Paolai area. The results of this study provide not only geoinfomatic datasets of the hazardous area, but also for essential geomorphologic information for other study, and for hazard mitigation and planning, as well.</p>

scholarly journals Surveying Wild Animals from Satellites, Manned Aircraft and Unmanned Aerial Systems (UASs): A Review

2019 ◽  
Vol 11 (11) ◽  
pp. 1308 ◽  
Author(s):  
Dongliang Wang ◽  
Quanqin Shao ◽  
Huanyin Yue
Keyword(s):  
High Resolution ◽  
Satellite Image ◽  
Wild Animal ◽  
Unmanned Aircraft ◽  
Image Resolution ◽  
Detection Methods ◽  
Unmanned Aerial Systems ◽  
Wild Animals ◽  
Small Areas ◽  
Aerial Surveys

This article reviews studies regarding wild animal surveys based on multiple platforms, including satellites, manned aircraft, and unmanned aircraft systems (UASs), and focuses on the data used, animal detection methods, and their accuracies. We also discuss the advantages and limitations of each type of remote sensing data and highlight some new research opportunities and challenges. Submeter very-high-resolution (VHR) spaceborne imagery has potential in modeling the population dynamics of large (>0.6 m) wild animals at large spatial and temporal scales, but has difficulty discerning small (<0.6 m) animals at the species level, although high-resolution commercial satellites, such as WorldView-3 and -4, have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode. This situation will not change unless the satellite image resolution is greatly improved in the future. Manned aerial surveys have long been employed to capture the centimeter-scale images required for animal censuses over large areas. However, such aerial surveys are costly to implement in small areas and can cause significant disturbances to wild animals because of their noise. In contrast, UAS surveys are seen as a safe, convenient and less expensive alternative to ground-based and conventional manned aerial surveys, but most UASs can cover only small areas. The proposed use of UAS imagery in combination with VHR satellite imagery would produce critical population data for large wild animal species and colonies over large areas. The development of software systems for automatically producing image mosaics and recognizing wild animals will further improve survey efficiency.

scholarly journals Satellite Determination of Peatland Water Table Temporal Dynamics by Localizing Representative Pixels of A SWIR-Based Moisture Index

2020 ◽  
Vol 12 (18) ◽  
pp. 2936
Author(s):  
Iuliia Burdun ◽  
Michel Bechtold ◽  
Valentina Sagris ◽  
Annalea Lohila ◽  
Elyn Humphreys ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Table ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Root Zone ◽  
Pearson Correlation ◽  
Remotely Sensed ◽  
Rooting Depth ◽  
Surface Model

The OPtical TRApezoid Model (OPTRAM) is a physically-based approach for remote soil moisture estimation. OPTRAM is based on the response of short-wave infrared (SWIR) reflectance to vegetation water status, which in turn responds to changes of root-zone soil moisture. In peatlands, the latter is tightly coupled to water table depth (WTD). Therefore, in theory, the OPTRAM index might be a useful tool to monitor WTD dynamics in peatlands, although the sensitivity of OPTRAM index to WTD changes will likely depend on vegetation cover and related rooting depth. In this study, we aim at identifying those locations (further called ‘best pixels’) where the OPTRAM index is most representative of overall peatland WTD dynamics. In peatlands, the high saturated hydraulic conductivity of the upper layer largely synchronizes the temporal WTD fluctuations over several kilometers, i.e., even though the mean and amplitude of the WTD dynamics may vary in space. Therefore, it can be assumed that the WTD time series, either measured at a single location or simulated for a grid cell with the PEATland-specific adaptation of the NASA Catchment Land Surface Model (PEATCLSM), are representative of the overall peatland WTD dynamics. We took advantage of this concept to identify the ‘best pixel’ of all spatially distributed OPTRAM pixels within a peatland, as that pixel with the highest time series Pearson correlation (R) with WTD data accounting for temporal autocorrelation. The OPTRAM index was calculated based on various remotely sensed images, namely, Landsat, MODIS, and aggregated Landsat images at MODIS resolution for five northern peatlands with long-term WTD records, including both bogs and fens. The ‘best pixels’ were dominantly covered with mosses and graminoids with little or no shrub or trees. However, the performance of OPTRAM highly depended on the spatial resolution of the remotely sensed data. The Landsat-based OPTRAM index yielded the highest R values (mean of 0.7 across the ‘best pixels’ in five peatlands). Our study further indicates that, in the absence of historical in situ data, PEATCLSM can be used as an alternative to localize ‘best pixels’. This finding enables the future applicability of OPTRAM to monitor WTD changes in peatlands on a global scale.

scholarly journals Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network

2021 ◽  
Vol 13 (23) ◽  
pp. 4803
Author(s):  
Sani Success Ojogbane ◽  
Shattri Mansor ◽  
Bahareh Kalantar ◽  
Zailani Bin Khuzaimah ◽  
Helmi Zulhaidi Mohd Shafri ◽  
...  
Keyword(s):  
High Resolution ◽  
City Planning ◽  
Smart Cities ◽  
Airborne Lidar ◽  
Aerial Imagery ◽  
Surface Model ◽  
Morphological Operations ◽  
Building Structures ◽  
Building Detection ◽  
Stream Channels

The detection of buildings in the city is essential in several geospatial domains and for decision-making regarding intelligence for city planning, tax collection, project management, revenue generation, and smart cities, among other areas. In the past, the classical approach used for building detection was by using the imagery and it entailed human–computer interaction, which was a daunting proposition. To tackle this task, a novel network based on an end-to-end deep learning framework is proposed to detect and classify buildings features. The proposed CNN has three parallel stream channels: the first is the high-resolution aerial imagery, while the second stream is the digital surface model (DSM). The third was fixed on extracting deep features using the fusion of channel one and channel two, respectively. Furthermore, the channel has eight group convolution blocks of 2D convolution with three max-pooling layers. The proposed model’s efficiency and dependability were tested on three different categories of complex urban building structures in the study area. Then, morphological operations were applied to the extracted building footprints to increase the uniformity of the building boundaries and produce improved building perimeters. Thus, our approach bridges a significant gap in detecting building objects in diverse environments; the overall accuracy (OA) and kappa coefficient of the proposed method are greater than 80% and 0.605, respectively. The findings support the proposed framework and methodologies’ efficacy and effectiveness at extracting buildings from complex environments.

scholarly journals Identifying Salt Marsh Shorelines from Remotely Sensed Elevation Data and Imagery

2019 ◽  
Vol 11 (15) ◽  
pp. 1795 ◽  
Author(s):  
Amy S. Farris ◽  
Zafer Defne ◽  
Neil K. Ganju
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Salt Marshes ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Horizontal Resolution ◽  
Unmanned Aircraft ◽  
Maximum Slope ◽  
Marsh Edge ◽  
Elevation Data

Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The marsh edge from elevation data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the “marsh scarp.” It is detected as the maximum slope along a cross-shore transect between mean high water and mean tide level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA. The other method to calculate the salt marsh shoreline is the marsh edge by image processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The two methods were compared to each other using high resolution unmanned aircraft systems (UAS) data, and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods, and less than 0.43 meters from the digitized shoreline. The MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability.

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