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 Remote Estimation of Vegetation Fraction and Flower Fraction in Oilseed Rape with Unmanned Aerial Vehicle Data

2016 ◽  
Vol 8 (5) ◽  
pp. 416 ◽  
Author(s):  
Shenghui Fang ◽  
Wenchao Tang ◽  
Yi Peng ◽  
Yan Gong ◽  
Can Dai ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Oilseed Rape ◽  
Vegetation Fraction ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Remote Estimation

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 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.

scholarly journals Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle

2017 ◽  
Vol 10 (5) ◽  
pp. 1-13 ◽  
Author(s):  
Du Mengmeng ◽  
◽  
Noguchi Noboru ◽  
Itoh Atsushi ◽  
Shibuya Yukinori ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Wheat Growth ◽  
Multi Temporal ◽  
Aerial Vehicle

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.

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