scholarly journals Comparison of Accuracy Aerial Photography UAV (Unmanned Aerial Vehicle) and GNSS (Global Navigation Satelitte System) for Mapping of Lambarih Village, Aceh Besar, Aceh

2020 ◽  
Vol 9 (3) ◽  
pp. 78-83
Author(s):  
Marwan A Bakar ◽  
Freddy Sapta Wirandha ◽  
Nizzamuddin Nizzamuddin ◽  
Febrian Fitryanik Susanta
Keyword(s):  
Data Processing ◽  
Control Point ◽  
Observation Point ◽  
Static Method ◽  
Aerial Photographs ◽  
Time Interval ◽  
Initial Stage ◽  
Aerial Vehicle ◽  
Global Navigation ◽  
Gnss Data

UAV (Unmaned Aerial Vehicle) atau yang biasa disebut drone saat ini telah banyak digunakan untuk pemetaan wilayah di Indonesia. Salah satu metode penentuan posisi satelit GNSS (Global Navigation Satelitte System) yaitu dengan metode statik. Penelitian ini mengkaji perbadingan ketelitian foto udara UAV dan foto udara UAV yang telah diikatkan dengan GNSS. Tahapan awal dalam penelitian ini adalah dilakukan pengambilan foto udara UAV wilayah Gampong Lambarih, kemudian dilakukan pengambilan data GNSS di lapangan sebanyak 4 titik di kawasan Gampong Lambarih, Aceh Besar dengan menggunakan titik kontrol atau titik ikat stasiun CBDA dengan doy 040 di Jantho. Tiap titik pengamatan dilakukan selama 30 menit dengan interval waktu pengukuran 1 detik. Pengolahan data UAV menggunakan aplikasi Agisoft dan pengolahan data GNSS menggunakan aplikasi HiTarget Geomatic Office (HGO) dan Website BIG. Hasil penelitian menunjukkan foto udara UAV yang diikat dengan GNSS memiliki ketelitian yang lebih tinggi yaitu mecapai ketelitian orde mm. UAV (Unmaned Aerial Vehicle) or what is commonly called a drone is currently widely used for regional mapping in Indonesia. One method of determining the position of the GNSS (Global Navigation Satelitte System) satellite is the static method. This study examines the comparison of the accuracy of UAV aerial photographs and UAV aerial photographs that have been tied to GNSS. The initial stage in this research was to take aerial photographs of the UAV of the Lambarih Village area. Then the GNSS data collection was carried out in the field as many as 4 points in the Gampong Lambarih area, Aceh Besar using the control point or tie point of the CBDA station with doy 040 in Jantho. Each observation point was carried out for 30 minutes with a measurement time interval of 1 second. UAV data processing uses the Agisoft application and GNSS data processing using the HiTarget Geomatic Office (HGO) application and the BIG Website. The results showed that aerial photographs of UAVs bound with GNSS had a higher accuracy reaching in order of mm. Keywords: GNSS, UAV, Statik, BIG, HGO

scholarly journals POSITIONAL ACCURACY OF AEROPHOTOGRAMMETRIC SURVEY IN THE PANTANAL DERIVED FROM UAV

2018 ◽  
Vol 37 (1) ◽  
pp. 137-146
Author(s):  
Anny Keli Aparecida Alves CÂNDIDO ◽  
Antonio Conceição PARANHOS FILHO ◽  
José MARCATO JÚNIOR ◽  
Normandes Matos da SILVA ◽  
Marcelo Ricardo HAUPENTHAL ◽  
...  
Keyword(s):  
Electric Propulsion ◽  
Control Point ◽  
Aerial Photographs ◽  
Control Points ◽  
Positional Accuracy ◽  
Mato Grosso ◽  
Gps Navigation ◽  
Remotely Piloted Aircraft ◽  
Aerial Vehicle ◽  
Mato Grosso Do Sul

The objective of this work was to analyze the geometric distortions of aerial photographs mosaic derived from unmanned aerial vehicle (UAV) generated with data from a GPS navigation engaged in remotely piloted aircraft in relation to the field collected control points with an RTK . The imaging was performed on August 9, 2014, at UFMS research base in the Pantanal of Mato Grosso do Sul, with a fixed-wing aircraft and electric propulsion motor. The flight was conducted at an altitude of 150 m, planned in such a way to obtain pixels with about 4 cm spatial resolution. To check the accuracy of the mosaic were collected control points from a GNSS HIPER receiver. To ease the location of points on aerial photographs were made markings on the ground. Mosaic without control point presented displacement ranging from 6.30 m to 8,83m. Previously the tiles with 1, 2 and 3 control points had low errors, given the legislation for the georeferencing of rural properties. Products generated from controls points have high planialtimetric accuracy and are likely to use in jobs that require high positional accuracy, such as expertise services, registration and georeferencing of rural properties, crops analysis and flora and fauna quantification.

scholarly journals Special Issue on GNSS Data Processing and Navigation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4119
Author(s):  
Adria Rovira-Garcia ◽  
José Miguel Juan Zornoza
Keyword(s):  
Data Processing ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Special Issue ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Gnss Data

Global Navigation Satellite System (GNSS) data can be used in a myriad of ways. The current number of applications exceed by far those originally GNSS was designed for. As an example, the present Special Issue on GNSS Data Processing and Navigation compiles 14 international contributions covering several aspects of GNSS research. This Editorial summarizes the whole special issue grouping the contributions under four different, but related topics.

scholarly journals A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

2019 ◽  
Vol 9 (22) ◽  
pp. 4813 ◽  
Author(s):  
Hanbo Yang ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Zheng Sun ◽  
Xuesong Mei
Keyword(s):  
Time Windows ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Time Interval ◽  
Kernel Module ◽  
Initial Stage ◽  
Research Task ◽  
Consecutive Time ◽  
Useful Life

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

The PPP data processing algorithm to create geodetic networks

2021 ◽  
pp. 464-468
Author(s):  
A.D. Tikhonov ◽  
A.A. Kochiev
Keyword(s):  
Data Processing ◽  
Processing Algorithm ◽  
Navigation Systems ◽  
Geodetic Networks ◽  
Data Processing Algorithm ◽  
Global Navigation ◽  
Global Navigation Systems

The article deals with determination of coordinates using global navigation systems, and application of the PPP data processing algorithm to obtain coordinates. The authors conducted an experiment illustrating the algorithm accuracy.

METEOROLOGICAL GNSS APPLICATION FOR HEAVY RAIN ON DECEMBER 31, 2019 IN THE JAKARTA AND SURROUNDING AREAS

2021 ◽  
pp. 273
Author(s):  
Syachrul Arief ◽  
Ihsan Muhamad Muafiry
Keyword(s):  
Water Vapor ◽  
Data Processing ◽  
Average Distance ◽  
Heavy Rain ◽  
Research Area ◽  
High Rainfall ◽  
Interesting Pattern ◽  
Surrounding Areas ◽  
Gnss Data ◽  
Second Peak

This study aims to utilize GNSS for meteorology in Indonesia. With the "goGPS" software, the zenith troposphere delay (ZTD) value is estimated. Calculations in rainy conditions, the ZTD value is converted into a water vapor value (PWV). The research area for the phenomenon of heavy rain occurred at the end of 2019 in Jakarta and its surroundings, which caused flooding on January 1, 2020. According to the Geophysical Meteorology and Climatology Agency (BMKG), the flood's primary cause was high rainfall. Meanwhile, the rainfall at Taman Mini and Jatiasih stations was 335 mm/day and 260 mm/day, respectively. We get an interesting pattern of PWV values for this rain phenomenon. GNSS data processing, the PWV value at five GNSS stations around Jakarta, shows the same pattern even though the average distance between GNSS stations is ~ 30 km. The PWV value appears to increase at noon on December 30, 2019, and the peak occurs in the early hours of December 31, 2019. The PWV value suddenly decreases at noon on January 1, 2020. Next, the PWV value increases again but not as high as at the previous peak. Since January 2, 2020, the PWV value has decreased and remained almost constant until January 4, 2020. In that period, there were two events that the PWV value increased. The PWV value at the first peak is ~ 70 mm, and at the second peak ~ 65 mm. The most significant increase in PWV value was recorded at CJKT stations.

scholarly journals Glacier geometry and elevation changes on Svalbard (1936–90): a baseline dataset

2007 ◽  
Vol 46 ◽  
pp. 106-116 ◽  
Author(s):  
C. Nuth ◽  
J. Kohler ◽  
H.F. Aas ◽  
O. Brandt ◽  
J.O. Hagen
Keyword(s):  
Aerial Survey ◽  
Aerial Photographs ◽  
Time Interval ◽  
Topographic Map ◽  
Elevation Change ◽  
Regional Variability ◽  
Random Components ◽  
Elevation Changes ◽  
Norwegian Polar Institute ◽  
Elevation Model

AbstractThis study uses older topographic maps made from high-oblique aerial photographs for glacier elevation change studies. We compare the 1936/38 topographic map series of Svalbard (Norwegian Polar Institute) to a modern digital elevation model from 1990. Both systematic and random components of elevation error are examined by analyzing non-glacier elevation difference points. The 1936/38 photographic aerial survey is examined to identify areas with poor data coverage over glaciers. Elevation changes are analyzed for seven regions in Svalbard (~5000 km2), where significant thinning was found at glacier fronts, and elevation increases in the upper parts of the accumulation areas. All regions experience volume losses and negative geodetic balances, although regional variability exists relating to both climate and topography. Many surges are apparent within the elevation change maps. Estimated volume change for the regions is –1.59±0.07km3 a–1 (ice equivalent) for a geodetic annual balance of –0.30ma–1w.e., and the glaciated area has decreased by 16% in the 54 year time interval. The 1936–90 data are compared to modern elevation change estimates in the southern regions, to show that the rate of thinning has increased dramatically since 1990.

scholarly journals Pemanfaatan Metode Fotogrametri untuk Pengukuran Garis Pantai dan Identifikasi Objek-Objek Tematik dengan Menggunakan Wahana Uav (Unmanned Aerial Vehicle) (Studi Kasus Pengukuran Garis Pantai di Pangkalan TNI AL Pondok Dayung

2020 ◽  
Vol 5 (1) ◽  
pp. 71-84
Author(s):  
Adhyta Harfan ◽  
Dipo Yudhatama ◽  
Imam Bachrodin
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Control Point ◽  
Ground Control ◽  
Ground Control Point ◽  
Real Time Kinematic ◽  
Aerial Vehicle

Metode Fotogrametri telah banyak digunakan dalam survei dan pemetaan. Seiring dengan kemajuan ilmu pengetahuan dan teknologi, metode fotogrametri saat ini berbasiskan pesawat tanpa awak atau yang lebih dikenal dengan UAV (Unmanned Aerial Vehicle). Kelebihan metode fotogrametri berbasiskan UAV untuk pengukuran garis pantai adalah memiliki resolusi spasial yang sangat tinggi dan dapat menjagkau daerah-daerah yang sulit dan berbahaya. Di samping itu juga dapat memberikan data foto udara terkini dengan sekala detail. Dalam penelitian ini membandingkan ketelitian horisontal antara hasil pengukuran garis pantai menggunakan metode fotogrametri berbasiskan UAV secara rektifikasi dengan GCP (Ground Control Point) maupun secara PPK (Post Processed Kinematic) dengan pengukuran garis pantai metode GNSS RTK (Real Time Kinematic). Hasil perhitungan ketelitian horisontal mengacu pada standar publikasi IHO S-44 tentang pengukuran garis pantai. Pemotretan dilakukan dengan ketinggian terbang 180 m, dengan tampalan depan dan samping 80%. Hasil perhitungan ketelitian horisontal foto udara terektifikasi 5 GCP, foto udara PPK dan foto udara PPK terektifikasi 1 GCP terhadap pengukuran garis pantai dengan metode GNSS RTK diperoleh nilai standar deviasi (σ) dan 95% selang kepercayaan (CI95%) masing-masing sebagai berikut: σ5gcp=10,989 cm dengan CI95% 16.8 cm < μ < 21.2 cm , σppk=26,066 cm dengan CI95% 26.5 cm < μ < 37 cm dan σppk1gcp=10,378 cm dengan CI95% 15.6 cm < μ < 19.8 cm. Kemudian terdapat 10 objek tematik berdasarkan Peta Laut Nomor 1 yang dapat diinterpretasi pada hasil orthomosaic foto udara.

Illumination Correction of Multi-Time RGB Images Obtained with an Unmanned Aerial Vehicle

2021 ◽  
pp. 50-58
Author(s):  
Michael Yu. Kataev ◽  
Maria M. Dadonova ◽  
Dmitry S. Efremenko
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Digital Camera ◽  
Quantitative Result ◽  
Efficient Solutions ◽  
Acquisition Time ◽  
Time Interval ◽  
Agricultural Field ◽  
Aerial Vehicle ◽  
Rgb Images

The goal of this research was to study and optimize multi-temporal RGB images obtained by a UAV (unmanned aerial vehicle). A digital camera onboard the UAV allows obtaining data with a high temporal and spatial resolution of ground objects. In the case considered by us, the object of study is agricultural fields, for which, based on numerous images covering the agricultural field, image mosaics (orthomosaics) are constructed. The acquisition time for each orthomosaic takes at least several hours, which imposes a change in the illuminance of each image, when considered separately. Orthomosaics obtained in different periods of the year (several months) will also differ from each other in terms of illuminance. For a comparative analysis of different parts of the field (orthomosaic), obtained in the same time interval or comparison of areas for different periods of time, their alignment by illumination is required. Currently, the majority of alignment approaches rely rather on colour (RGB) methods, which cannot guarantee finding efficient solutions, especially when it is necessary to obtain a quantitative result. In the paper, a new method is proposed that takes into account the change in illuminance during the acquisition of each image. The general formulation of the problem of light correction of RGB images in terms of assessing the colour vegetation index Greenness is considered. The results of processing real measurements are presented.

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