scholarly journals Using Unmanned Aerial Vehicle Remote Sensing and a Monitoring Information System to Enhance the Management of Unauthorized Structures

2019 ◽  
Vol 9 (22) ◽  
pp. 4954
Author(s):  
Yuanrong He ◽  
Weiwei Ma ◽  
Zelong Ma ◽  
Wenjie Fu ◽  
Chihcheng Chen ◽  
...  
Keyword(s):  
Information System ◽  
Unmanned Aerial Vehicle ◽  
Aerial Images ◽  
Surface Model ◽  
Ground Survey ◽  
Elevation Changes ◽  
Aerial Vehicle ◽  
New Construction ◽  
Gis Software ◽  
Interaction Interpretation

In this research, we investigated using unmanned aerial vehicle (UAV) photographic technology to prevent the further expansion of unauthorized construction and thereby reduce postdisaster losses. First, UAV dynamic aerial photography was used to obtain dynamic digital surface model (DSM) data and elevation changes of 2–8 m as the initial sieve target. Then, two periods of dynamic orthophoto images were superimposed for human–computer interaction interpretation, so we could quickly distinguish buildings undergoing expansion, new construction, or demolition. At the same time, mobile geographic information system (GIS) software was used to survey the field, and the information gathered was developed to support unauthorized construction detection. Finally, aerial images, interpretation results, and ground survey information were integrated and released on WebGIS to build a regulatory platform that can achieve accurate management and effectively prevent violations.

Shipping Container Counting Approach Using Unmanned Aerial Vehicle (UAV) and ArcGIS

2019 ◽  
Vol 16 (2) ◽  
pp. 15
Author(s):  
Afiq Abdullah ◽  
Jasmee Jaafar ◽  
Khairul Nizam Tahar ◽  
Mohamad Hezri Razali
Keyword(s):  
Information Processing ◽  
Unmanned Aerial Vehicle ◽  
Digital Terrain Model ◽  
Aerial Images ◽  
Geographical Information ◽  
Surface Model ◽  
Standard Size ◽  
Shipping Container ◽  
Counting Approach ◽  
Aerial Vehicle

In Malaysia, the existing of counting approach on the shipping container at depot is carried out by manual based system. This has made the efficiency of the method to be questioned which can be solved through automation. Under previous studies, Unmanned Aerial Vehicle (UAV) is demonstrated for automatic counting of cars and trees. Therefore, the possibility for shipping container counting is highly required in which promotes low-cost alternative and automatedpilot for data collection. Based on this study, the aerial images captured using UAV is combined with geographical information processing software, ArcGIS, towards automated approach for container counting. The overlapping aerial images are post-processed using photogrammetric technique to produce Digital Surface Model (DSM) that represents the ground and above surface feature’s elevations. Then, the constructed DSM is filtered to develop Digital Terrain Model (DTM) where it represents the ground surface’s elevation only. Then, container’s candidates are isolated using subtraction of the DTM from DSM to generate normalized DSM (nDSM) which represents the heights of container’s stacks. From the standard size and height of one container from ISO, the number of containers is extracted. The ModelBuilder tool available in ArcGIS is customized for automated geographical information processing. From results, the proposed approach contributed to 100% of counting accuracy. Keywords: Unmanned Aerial Vehicle, Counting, Shipping Container, ArcGIS, ModelBuilder

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

scholarly journals Utilizing drone technology in the civil engineering

2019 ◽  
Vol 14 (1) ◽  
pp. 27-37
Author(s):  
Matúš Tkáč ◽  
Peter Mésároš
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Civil Engineering ◽  
Aerial Images ◽  
General Overview ◽  
Aerial Photogrammetry ◽  
Different Types ◽  
Aerial Vehicle

Abstract An unmanned aerial vehicle (UAVs), also known as drone technology, is used for different types of application in the civil engineering. Drones as a tools that increase communication between construction participants, improves site safety, uses topographic measurements of large areas, with using principles of aerial photogrammetry is possible to create buildings aerial surveying, bridges, roads, highways, saves project time and costs, etc. The use of UAVs in the civil engineering can brings many benefits; creating real-time aerial images from the building objects, overviews reveal assets and challenges, as well as the broad lay of the land, operators can share the imaging with personnel on site, in headquarters and with sub-contractors, planners can meet virtually to discuss project timing, equipment needs and challenges presented by the terrain. The aim of this contribution is to create a general overview of the use of UAVs in the civil engineering. The contribution also contains types of UAVs used for construction purposes, their advantages and also disadvantages.

Natural superficial water storage and aquifer recharge assessment in Brazilian savanna wetland using unmanned aerial vehicle and geophysical survey

2020 ◽  
Vol 8 (3) ◽  
pp. 224-244
Author(s):  
Lucas Moreira Furlan ◽  
Vania Rosolen ◽  
Jepherson Salles ◽  
César Augusto Moreira ◽  
Manuel Eduardo Ferreira ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Agricultural Land ◽  
Water Volume ◽  
Surface Model ◽  
Aquifer Recharge ◽  
Flow Paths ◽  
Subsurface Structures ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Remote Sensing Techniques

Human pressure on the water resources provided by natural isolated wetlands has intensified in Brazil due to an increase in agricultural land equipped with irrigation. However, the amount of water stored in these areas and its contribution to aquifer recharge is unknown. This study aimed to quantify the amount of water that can be retained in a natural wetland and to propose a model of groundwater recharge. We used remote sensing techniques involving unmanned aerial vehicle to map the wetland and highlight its internal morphology, using a red–green–blue orthomosaic and a digital surface model. The 2-D inversion and a pseudo-3-D model from electrical resistivity tomography data were used to visualize the subsurface structures and hydrologic flow paths. The wetland is a reservoir storing up to 416.996 m3 of water during the rainy months. Distinct internal compartments characterize the wetland topography and different water-volume storage, lower in the border and higher in the center. A leakage point connects surface water to groundwater through direct vertical flow, which constitutes the aquifer recharge zone. Remotely sensed very high-resolution images allied with geophysical techniques allowed complete surface and subsurface imaging and offered visual tools that contributed to understanding the hydrodynamics of the wetland.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

Fast Data Acquisition of Aerial Images Using Unmanned Aerial Vehicle System

2014 ◽  
Vol 3 (3) ◽  
pp. 162
Author(s):  
Norhadija Darwin ◽  
Anuar Ahmad
Keyword(s):  
Data Acquisition ◽  
Root Mean Square ◽  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Digital Camera ◽  
Aerial Images ◽  
Mean Square ◽  
Fast Data Acquisition ◽  
Vehicle System ◽  
Aerial Vehicle

The present work discusses the technique and methodology of analysing the potential of fast data acquisition of aerial images using unmanned aerial vehicle system. This study utilizes UAV system for large scale mapping by using digital camera attached to the UAV. UAV is developed from the low-altitude photogrammetric mapping to perform the accuracy of the aerial photography and the resolution of the image. The Ground Control Points (GCPs) and Check Points (CPs) are established using Rapid Static techniques through GPS observation for registration purpose in photogrammetric process. The GCPs is used in the photogrammetric processes to produce photogrammetric output while the CP is employed for accuracy assessment. A Pentax Optio W90 consumer digital camera is also used in image acquisition of the aerial photograph. Besides, this study also involves image processing and map production using Erdas Imagine 8.6 software. The accuracy of the orthophoto is determined using the equation of Root Mean Square Error (RMSE). The final result from orthophoto is compared to the ground survey using total station to show the different accuracy of DEM and planimetric survey. It is discovered that root mean square errors obtained from UAV system are ± 0.510, ± 0.564 and ± 0.622 for coordinate x, y and z respectively. Hence, it can be concluded that the accuracy obtained from UAV system is achieved in sub meter. In a nutshell, UAV system has potential use for large scale mapping in field of surveying and other diversified environmental applications especially for small area which has limited time and less man power.

scholarly journals Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Migratory Bird ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Detection Methods ◽  
Single Shot ◽  
Convolutional Network ◽  
Aerial Vehicle ◽  
Bird Detection

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

scholarly journals Exploring the contributions of vegetation and dune size to early dune development using unmanned aerial vehicle (UAV) imaging

2017 ◽  
Vol 14 (23) ◽  
pp. 5533-5549 ◽  
Author(s):  
Marinka E. B. van Puijenbroek ◽  
Corjan Nolet ◽  
Alma V. de Groot ◽  
Juha M. Suomalainen ◽  
Michel J. P. M. Riksen ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Type ◽  
Grass Species ◽  
Surface Model ◽  
Maximum Height ◽  
Vegetation Density ◽  
Vegetation Height ◽  
Erosion Processes ◽  
Aerial Vehicle ◽  
Vegetation Characteristics

Abstract. Dune development along highly dynamic land–sea boundaries is the result of interaction between vegetation and dune size with sedimentation and erosion processes. Disentangling the contribution of vegetation characteristics from that of dune size would improve predictions of nebkha dune development under a changing climate, but has proven difficult due to the scarcity of spatially continuous monitoring data. This study explored the contributions of vegetation and dune size to dune development for locations differing in shelter from the sea. We monitored a natural nebkha dune field of 8 ha, along the coast of the island Texel, the Netherlands, for 1 year using an unmanned aerial vehicle (UAV) with camera. After constructing a digital surface model and orthomosaic we derived for each dune (1) vegetation characteristics (species composition, vegetation density, and maximum vegetation height), (2) dune size (dune volume, area, and maximum height), (3) degree of shelter (proximity to other nebkha dunes and the sheltering by the foredune). Changes in dune volume over summer and winter were related to vegetation, dune size and degree of shelter. We found that a positive change in dune volume (dune growth) was linearly related to initial dune volume over summer but not over winter. Big dunes accumulated more sand than small dunes due to their larger surface area. Exposed dunes increased more in volume (0.81 % per dune per week) than sheltered dunes (0.2 % per dune per week) over summer, while the opposite occurred over winter. Vegetation characteristics did not significantly affect dune growth in summer, but did significantly affect dune growth in winter. Over winter, dunes dominated by Ammophila arenaria, a grass species with high vegetation density throughout the year, increased more in volume than dunes dominated by Elytrigia juncea, a grass species with lower vegetation density (0.43 vs. 0.42 (m3 m−3) week−1). The effect of species was irrespective of dune size or distance to the sea. Our results show that dune growth in summer is mainly determined by dune size, whereas in winter dune growth was determined by vegetation type. In our study area the growth of exposed dunes was likely restricted by storm erosion, whereas growth of sheltered dunes was restricted by sand supply. Our results can be used to improve models predicting coastal dune development.

scholarly journals Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle

2017 ◽  
Vol 9 (5) ◽  
pp. 417 ◽  
Author(s):  
Peter Roosjen ◽  
Juha Suomalainen ◽  
Harm Bartholomeus ◽  
Lammert Kooistra ◽  
Jan Clevers
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Images ◽  
Reflectance Anisotropy ◽  
Aerial Vehicle
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