scholarly journals THE USE OF UNMANNED AERIAL VEHICLE TO IMPROVE THE EFFICIENCY OF NATURE PLAGUE FOCUS MONITORING

2018 ◽  
pp. 52-56
Author(s):  
V.M. Dubyanskiy ◽  
N.V. Tsapko ◽  
L.I. Shaposhnikova ◽  
D.Yu. Degtyarev ◽  
N.A. Davydova ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
New Technologies ◽  
Small Diameter ◽  
Aerial Vehicle

New technologies based on remote sensing, has been actively developing now for the increasing the effectiveness of nature plague foci monitoring. The unmanned aerial vehicle (UAV) «GeoScan–101» was used for monitoring and for orthophoto creating. The monitoring was carried out at Caspian nature plague focus on a stationary square 4 ha. The accounting of rodents’ holes had been implemented previously. Gerbils Meriones tamariscinus is the host of plague microbe in the Caspian natural plague focus. The monitoring using UAV allows identifying 78,26 % of Meriones tamariscinus holes as well as Microtus socialis and Ellobius talpinus. The comprehensive using remote sensing from space and data from drone (a high-resolution picture in real time) for elaboration data of remote sensing allows increasing the effectiveness of nature plague foci survey, where the hosts have the holes small diameter: the gerbils' genus Meriones and the voles’ genus Microtus.

scholarly journals Monitoring vineyards with UAV and multi-sensors for the assessment of water stress and grape maturity

2017 ◽  
Vol 5 (2) ◽  
pp. 37-50 ◽  
Author(s):  
I. Soubry ◽  
P. Patias ◽  
V. Tsioukas
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Efficient Solution ◽  
Grape Variety ◽  
Agricultural Applications ◽  
Aerial Vehicle ◽  
Remote Sensing Indices

This paper deals with the monitoring of vineyards for the assessment of water stress and grape maturity using an unmanned aerial vehicle (UAV) equipped with multispectral/infrared and red-green-blue (RGB) cameras. The study area is the Gerovassiliou winery in the region of Epanomi, Greece, cultivated with the local grape variety of Malagouzia. Fifteen flights were conducted with a fixed-wing UAV during the months of April to August 2015 with a mean interval of 2 weeks. The flight images were photogrammetrically processed for the production of orthoimages and then used to extract indices for the detection of water stress. Grape samples were collected 2 days before harvest and then analyzed and correlated with remote sensing indices. The TCARI/OSAVI index showed the best correlation with the grape samples with regards to maturity and the likelihood of water stress. Furthermore, the final results were of high resolution as far as farm purposes are concerned (a scale of 1:500 for all three sensors). These facts suggest that the instruments used in this study represent a fast, reliable, and efficient solution to the evaluation of crops for agricultural applications.

scholarly journals Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

2020 ◽  
Vol 12 (1) ◽  
pp. 182 ◽  
Author(s):  
Lingxuan Meng ◽  
Zhixing Peng ◽  
Ji Zhou ◽  
Jirong Zhang ◽  
Zhenyu Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Detection System ◽  
Detection Model ◽  
Aerial Vehicle ◽  
Real Time Detection ◽  
Pipeline Safety

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

scholarly journals Unmanned Aerial Vehicle for Remote Sensing Applications—A Review

2019 ◽  
Vol 11 (12) ◽  
pp. 1443 ◽  
Author(s):  
Huang Yao ◽  
Rongjun Qin ◽  
Xiaoyu Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Future Research ◽  
Systematic Analysis ◽  
Sensing Applications ◽  
Aerial Vehicle

The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.

scholarly journals Mapping Plastic Mulched Farmland for High Resolution Images of Unmanned Aerial Vehicle Using Deep Semantic Segmentation

2019 ◽  
Vol 11 (17) ◽  
pp. 2008 ◽  
Author(s):  
Qinchen Yang ◽  
Man Liu ◽  
Zhitao Zhang ◽  
Shuqin Yang ◽  
Jifeng Ning ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Classification Method ◽  
Remote Sensing Images ◽  
Segmentation Methods ◽  
Traditional Classification ◽  
Aerial Vehicle ◽  
Segmentation Models

With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images.

scholarly journals Vegetation Monitoring Using UAV : a Preliminary Study

2018 ◽  
Vol 7 (4.35) ◽  
pp. 223 ◽  
Author(s):  
Soon Eng L ◽  
Rozita Ismail ◽  
Wahidah Hashim ◽  
Rajina R. Mohamed ◽  
Aslina Baharum
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Post Processing ◽  
Vegetation Monitoring ◽  
Live Video ◽  
Current Trends ◽  
Military Research ◽  
Aerial Vehicle ◽  
Preliminary Study

Remote sensing using drone or UAV (unmanned aerial vehicle) is the current trends and this technology can provide unrevealed life-altering benefits to mankind. Drones are being used in many sectors such as for military, research, agricultural and recreational means. This technology not only can reduce the time of inspection, but it is also giving many benefits such as provides real-time live video for site inspection that can help user to analyze site logistic and speeding up the overall tasks. However, vegetation monitoring using remote sensing has its own challenges in terms of processing the captured image and data. Somehow, previous research has suggested a lot of different possible algorithm that could be used for post-processing the data gathered. Nevertheless, most of the algorithm requires a specific sensor in order to get the result. The objective of this paper is to identify and verify the algorithm that is suitable to process the vegetation image. This research will use the data gathered from various area by using consumer camera and process by using Visible Atmospherically Resistant Index (VARI) indices. Finally, this research will observe the accuracy of the result analyzed using the VARI and identify the characteristic of the algorithm.

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