Analysis of Landslide Kinematics Using Multi-temporal Unmanned Aerial Vehicle Imagery, La Honda, California

2019 ◽  
pp. 1-17 ◽  
Author(s):  
Jordan A. Carey ◽  
Nicholas Pinter ◽  
Alexandra J. Pickering ◽  
Carol S. Prentice ◽  
Stephen B. Delong
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Slope Failure ◽  
Cost Effective ◽  
Horizontal Displacement ◽  
Terrestrial Lidar ◽  
Displacement Vectors ◽  
Cost Effective Method ◽  
Multi Temporal ◽  
Aerial Vehicle ◽  
Landslide Kinematics

Abstract The combination of unmanned aerial vehicle (UAV) photography with structure-from-motion (SfM) digital photogrammetry provides a quickly deployable and cost-effective method for monitoring geomorphic change, particularly for hazards such as landslides. The Scenic Drive landslide is a deep-seated slope failure in La Honda, CA, with episodic activity in 1998 and 2005–06. Heavy rainfall during 2016–17 initiated movement of a new and separate landslide directly upslope of the existing Scenic Drive landslide, damaging three residences. We acquired imagery of the Upper Scenic Drive landslide beginning 2 days after initial motion using a global positioning system–enabled UAV. We used this imagery to generate seven digital elevation models (DEMs) between January and May 2017, with spatial resolutions of ∼3–10 cm/pixel. We compared these DEMs with each other and with available light detection and ranging (LiDAR) data to assess landslide kinematics, including horizontal displacement vectors, rates of motion, and total mass redistribution, and to test the accuracy and applicability of UAV/SfM-derived measurements. We estimated the maximum horizontal displacement of the slide was at least 5 m during the monitoring period and calculated that ∼3,000 m3 of material was displaced by the landslide. Comparing the UAV-derived topography with synchronous terrestrial LiDAR scanning showed that accuracies of the two techniques are comparable, generally within 0.05 m horizontally and within 0.20 m vertically in unvegetated areas. This study demonstrates the capability of combining UAV and SfM to map and monitor active geomorphic processes in emergent situations where high-resolution digital topography is needed in near-real-time.

scholarly journals Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction

2016 ◽  
Vol 41 (2) ◽  
pp. 126-137 ◽  
Author(s):  
Hee Sup Yun ◽  
Soo Hyun Park ◽  
Hak-Jin Kim ◽  
Wonsuk Daniel Lee ◽  
Kyung Do Lee ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Fraction ◽  
Multi Temporal ◽  
Aerial Vehicle

scholarly journals Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

scholarly journals Slope Mapping using Unmanned Aerial Vehicle (UAV)

2021 ◽  
Vol 12 (3) ◽  
pp. 1781-1789
Author(s):  
Muhammad Farhan Zolkepli Et.al
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Cost Effective ◽  
Modern Method ◽  
Modern Technology ◽  
Surface Model ◽  
High Quality Image ◽  
Short Period ◽  
Aerial Vehicle ◽  
Normal Mapping ◽  
Digital Orthophoto

This paper discusses the applications of unmanned aerial vehicle (UAV) for slope mapping and also its important parameters including perimeter, area and also volume of certain selected area. With the development of modern technology, the utilization of UAV to gather data for slope mapping becoming easier as it is quick, reliable, precise, cost-effective and also easily to operate. Modern UAV able to take high quality image which essential for the effectiveness and nature of normal mapping output such as Digital Surface Model (DSM) and Digital Orthophoto. This photo captured by UAV will later transfer to commercial software to generate full map of study area. With the help of established software, the measurement of selected study areas can be determined easily which can be considered as the main interest in this study. In addition, another outcome of this study is, this modern method of mapping will be compare to traditional method of mapping which proven to be more effective in term of low costing, low time consuming, can gather huge amount of data within short period of time, low man power needed and almost no potential risk of hazardous effect to man.

scholarly journals Resizable Drone Designs: For Maneuvering through Paths of Varying Dimensions

2019 ◽  
Vol 8 (2) ◽  
pp. 3162-3166
Keyword(s):  
Embedded Systems ◽  
Unmanned Aerial Vehicle ◽  
Cost Effective ◽  
Search And Rescue ◽  
The Other ◽  
Complex Structures ◽  
Variable Size ◽  
Aerial Vehicle ◽  
Flying Robot ◽  
Flying Robots

An unmanned aerial vehicle, commonly known as a drone, is an aircraft without a human pilot aboard. Essentially, a drone is a flying robot that can be remotely controlled or fly autonomously through software-controlled flight plans in their embedded systems, Flying robots are increasingly adopted in search and rescue missions because of their capability to quickly collect and stream information from remote and dangerous areas. Their maneuverability and hovering capabilities allow them to navigate through complex structures, inspect damaged buildings, and even explore underground tunnels and caves. Since their size is fixed, maneuvering over the compact areas and tunnels of variable size becomes an issue. To overcome this issue, we propose a model of quadrotor design which has the capability to change its size. The arm length of the quadrotor is changed dynamically so that it can fly in areas of variable sizes that would be hard to reach with the quadrotor of fixed arm length. On the other hand, our model is cost-effective, since the arm of the drone is designed with PVC (Polyvinyl Chloride). Using this model, drones will be able to move over compact areas and passages of variable sizes, thus aiding in better exploration during search and rescue operations.

scholarly journals Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping

Agriculture ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 65 ◽  
Author(s):  
Robin Mink ◽  
Avishek Dutta ◽  
Gerassimos Peteinatos ◽  
Markus Sökefeld ◽  
Johannes Engels ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Weed Control ◽  
Unmanned Aerial Vehicle ◽  
Cirsium Arvense ◽  
Rumex Crispus ◽  
Site Specific ◽  
Multi Temporal ◽  
Aerial Vehicle

Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

2015 ◽  
Vol 132 ◽  
pp. 19-27 ◽  
Author(s):  
Francisco Agüera Vega ◽  
Fernando Carvajal Ramírez ◽  
Mónica Pérez Saiz ◽  
Francisco Orgaz Rosúa
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Multi Temporal ◽  
Aerial Vehicle ◽  
Sunflower Crop

scholarly journals ASSESSMENT OF LANDSLIDE-INDUCED DISPLACEMENT AND DEFORMATION OF ABOVE-GROUND OBJECTS USING UAV-BORNE AND AIRBORNE LASER SCANNING DATA

2019 ◽  
Vol IV-2/W5 ◽  
pp. 461-467 ◽  
Author(s):  
T. Zieher ◽  
M. Bremer ◽  
M. Rutzinger ◽  
J. Pfeiffer ◽  
P. Fritzmann ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Additional Information ◽  
3D Point Clouds ◽  
Airborne Laser ◽  
Displacement Vectors ◽  
Multi Temporal ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> Multi-temporal 3D point clouds acquired with a laser scanner can be efficiently used for an area-wide assessment of landslide-induced surface changes. In the present study, displacements of the Vögelsberg landslide (Tyrol, Austria) are assessed based on available data acquired with airborne laser scanning (ALS) in 2013 and data acquired with an unmanned aerial vehicle (UAV) equipped with a laser scanner (ULS) in 2018. Following the data pre-processing steps including registration and ground filtering, buildings are segmented and extracted from the datasets. The roofs, represented as multi-temporal 3D point clouds are then used to derive displacement vectors with a novel matching tool based on the iterative closest point (ICP) algorithm. The resulting mean annual displacements are compared to the results of a geodetic monitoring based on an automatic tracking total station (ATTS) measuring 53 retroreflective prisms across the study area every hour since May 2016. In general, the results are in agreement concerning the mean annual magnitude (ATTS: 6.4&amp;thinsp;cm within 2.2 years, 2.9&amp;thinsp;cm a<sup>&amp;minus;1</sup>; laser scanning data: 13.2&amp;thinsp;cm within 5.4 years, 2.4&amp;thinsp;cm a<sup>&amp;minus;1</sup>) and direction of the derived displacements. The analysis of the laser scanning data proved suitable for deriving long-term landslide displacements and can provide additional information about the deformation of single roofs.</p>

Optimal patterns of dynamic soaring with a small unmanned aerial vehicle

2016 ◽  
Vol 231 (9) ◽  
pp. 1593-1608 ◽  
Author(s):  
Duo-Neng Liu ◽  
Zhong-Xi Hou ◽  
Zheng Guo ◽  
Xi-Xiang Yang ◽  
Xian-Zhong Gao
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Trajectory Optimization ◽  
Horizontal Displacement ◽  
Optimization Method ◽  
Dynamic Soaring ◽  
Direct Collocation ◽  
Terminal Constraints ◽  
Aerial Vehicle ◽  
Optimal Dynamic ◽  
Trajectory Pattern

Like albatross, unmanned aerial vehicles can significantly make use of wind gradient to extract energy by the flight technique named dynamic soaring. The research aims to develop a general optimization method to compute all the possible patterns of dynamic soaring with a small unmanned aerial vehicle. A direct collocation approach based on the Runge-Kutta integrator is proposed to solve the trajectory optimization problem for dynamic soaring. The optimal dynamic soaring trajectories are classified into two patterns: closed trajectory pattern and travelling trajectory pattern by applying terminal constraints of zero horizontal displacement and a certain travelling direction, respectively. Using different terminal constrains for heading angle and initial guesses in the optimization process, the former pattern can be divided into two subtypes: O-shaped and 8-shaped trajectories, while the latter one is divided into C-shaped, α-shaped, S-shaped and Ω-shaped trajectories. The characteristics of these patterns and the correlation among patterns are analyzed and discussed.

scholarly journals Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network

2020 ◽  
Vol 12 (10) ◽  
pp. 1668 ◽  
Author(s):  
Quanlong Feng ◽  
Jianyu Yang ◽  
Yiming Liu ◽  
Cong Ou ◽  
Dehai Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Feature Fusion ◽  
Agricultural Landscapes ◽  
Sequential Dependency ◽  
Temporal Features ◽  
Multi Temporal ◽  
Aerial Vehicle

Vegetable mapping from remote sensing imagery is important for precision agricultural activities such as automated pesticide spraying. Multi-temporal unmanned aerial vehicle (UAV) data has the merits of both very high spatial resolution and useful phenological information, which shows great potential for accurate vegetable classification, especially under complex and fragmented agricultural landscapes. In this study, an attention-based recurrent convolutional neural network (ARCNN) has been proposed for accurate vegetable mapping from multi-temporal UAV red-green-blue (RGB) imagery. The proposed model firstly utilizes a multi-scale deformable CNN to learn and extract rich spatial features from UAV data. Afterwards, the extracted features are fed into an attention-based recurrent neural network (RNN), from which the sequential dependency between multi-temporal features could be established. Finally, the aggregated spatial-temporal features are used to predict the vegetable category. Experimental results show that the proposed ARCNN yields a high performance with an overall accuracy of 92.80%. When compared with mono-temporal classification, the incorporation of multi-temporal UAV imagery could significantly boost the accuracy by 24.49% on average, which justifies the hypothesis that the low spectral resolution of RGB imagery could be compensated by the inclusion of multi-temporal observations. In addition, the attention-based RNN in this study outperforms other feature fusion methods such as feature-stacking. The deformable convolution operation also yields higher classification accuracy than that of a standard convolution unit. Results demonstrate that the ARCNN could provide an effective way for extracting and aggregating discriminative spatial-temporal features for vegetable mapping from multi-temporal UAV RGB imagery.

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