scholarly journals Crop Field Classification using Data Fusion of Unmanned Aerial Vehicle (UAV) and Sentinel 2A satellite: the case of Oda Dhawata Kebele Cluster farmland, Oromia Region, Ethiopia

2021 ◽  
Author(s):  
Melkamu Demelash ◽  
Binyam Tesfaw ◽  
Degefie Tibebe
Keyword(s):  
Maximum Likelihood ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Field Data ◽  
Crop Field ◽  
Dense Point ◽  
Aerial Vehicle ◽  
Crop Types ◽  
Crop Classification ◽  
Sentinel 2A

Abstract Accurate crop classification using remote sensing based satellite imageries approach remains challenging due to mix in spectral signatures. Employing Unmanned Aerial Vehicle (UAV) together with satellite imageries is believed in improving crop classification at field. Accordingly, this study aims to evaluate the potential of UAV images by blending with Sentinel 2A satellite images for crop field classification in Ethiopian agricultural context. The main purpose of the blending is to upgrade and or improve the lower resolution of the data source that is the sentinel 2A data which was 10m resolution. In the study, UAV data was used and preprocessed. The preprocessing includes camera calibration, photo alignment, dense point cloud generation based on the estimated camera positioning of scouting crop types. Then, orthomosaic UAV image was generated from single dense point cloud. Then, the processed UAV data was fused with Sentinel 2A (medium resolution) satellite data using Gram Schmidt pan sharpening method.this method is the most approach that it can run large data sets of spatial resultions. For crop classification, the Random forest (RF) machine-learning algorithm and Maximum likelihood methods were applied. Apart from the UAV and S2A data, field data was collected for training the crop classification. The point field data was collected from Teff, Wheat, Faba bean, Barley and Sorghum crop fields The results show that RF classifier algorithm classifies the crop types with 94% overall accuracy whereas the Maximum likelihood classifier with 90% overall accuracy. This implies that fused image has a potential to be used for crop type classification together with relatively better classification technique with high accuracy level

scholarly journals A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner

2021 ◽  
Vol 13 (23) ◽  
pp. 4811
Author(s):  
Rudolf Urban ◽  
Martin Štroner ◽  
Lenka Línková
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Reference Points ◽  
New Method ◽  
Color Information ◽  
Dense Point ◽  
Aerial Vehicle ◽  
Systematic Shift ◽  
Precision Testing ◽  
Better Than

Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Automated Geolocation in Urban Environments Using a Simple Camera-Equipped Unmanned Aerial Vehicle: A Rapid Mapping Surveying Alternative?

2020 ◽  
Vol 9 (7) ◽  
pp. 425
Author(s):  
Dimitrios Trigkakis ◽  
George Petrakis ◽  
Achilleas Tripolitsiotis ◽  
Panagiotis Partsinevelos
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Local Coordinate System ◽  
Arbitrary Point ◽  
Urban Environments ◽  
Signal Interference ◽  
Rapid Mapping ◽  
Gnss Positioning ◽  
Area Of Interest ◽  
Aerial Vehicle

GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.

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.

scholarly journals Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 774 ◽  
Author(s):  
Sun ◽  
Wang ◽  
Ding ◽  
Lu ◽  
Sun
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Imaging System ◽  
Cloud Model ◽  
Tree Canopy ◽  
Coefficient Of Determination ◽  
Remote Measurement ◽  
Probability Density Estimation ◽  
Point Cloud Model ◽  
Aerial Vehicle

Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the range of 2.13 to 6.69 cm/px, the orchard point-cloud model had a measurement accuracy of 100.00% for the rows and 90.86% to 98.20% for the columns. The coefficient of determination, R2, was in the range of 0.8497 to 0.9376, 0.8103 to 0.9492, and 0.8032 to 0.9148, respectively, and the average relative error was in the range of 1.72% to 3.42%, 2.18% to 4.92%, and 7.90% to 13.69%, respectively, among the H, W, and V values measured manually and by UAV photogrammetry. The results showed that UAV visual imaging is suitable for 3D morphological remote canopy evaluations, facilitates orchard canopy informatization, and contributes substantially to efficient management and control of modern standard orchards.

scholarly journals UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

2019 ◽  
Vol IV-2/W5 ◽  
pp. 405-412 ◽  
Author(s):  
A. Mayr ◽  
M. Bremer ◽  
M. Rutzinger ◽  
C. Geitner
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Test Site ◽  
Satellite System ◽  
Alpine Grassland ◽  
Proof Of Concept ◽  
Aerial Vehicle ◽  
Geomorphological Changes ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (<i>bare earth</i>, <i>grassland</i>, <i>trees</i>), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&amp;thinsp;m<sup>3</sup> is estimated for the test site (48&amp;thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.</p>

A Simaplied Adaptive Kalman Filtering Algorithm

2014 ◽  
Vol 1037 ◽  
pp. 378-382
Author(s):  
Lei Bo ◽  
Xin Yan Zhu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Unmanned Aerial Vehicle ◽  
Kalman Filtering ◽  
Likelihood Estimation ◽  
Simplified Model ◽  
Filtering Algorithm ◽  
Aerial Vehicle ◽  
Kalman Filtering Algorithm ◽  
The Moment

The adaptive Kalman filtering algorithm was adopted in the online estimate of navigation state of unmanned aerial vehicle (UAV) as the simplified model often used. At the moment, the alogorithms those usually applied in this territory are not perfect. Analysed the adaptive Kalman filtering based on Maximum-Likelihood Estimation and Sage-Husa Kalman filtering, take advantage the characteristics of residue, choose the estimation windows, a simplified adaptive Kalman filtering algorithm was gived.

scholarly journals Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

2020 ◽  
Vol 12 (5) ◽  
pp. 885 ◽  
Author(s):  
Juan Picos ◽  
Guillermo Bastos ◽  
Daniel Míguez ◽  
Laura Alonso ◽  
Julia Armesto
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Single Point ◽  
Tree Height ◽  
Individual Tree ◽  
Eucalyptus Plantation ◽  
Local Maxima ◽  
Eucalypt Plantations ◽  
Aerial Vehicle ◽  
Precision Forestry

The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

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