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 How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?

2021 ◽  
Vol 13 (6) ◽  
pp. 1134
Author(s):  
Anas El-Alem ◽  
Karem Chokmani ◽  
Aarthi Venkatesan ◽  
Lhissou Rachid ◽  
Hachem Agili ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Unmanned Aerial Vehicle ◽  
Optical Sensors ◽  
Regional Scale ◽  
Turbid Waters ◽  
Vehicle Data ◽  
Freshwater Bodies ◽  
Aerial Vehicle ◽  
Leave One Out ◽  
Semi Empirical

Optical sensors are increasingly sought to estimate the amount of chlorophyll a (chl_a) in freshwater bodies. Most, whether empirical or semi-empirical, are data-oriented. Two main limitations are often encountered in the development of such models. The availability of data needed for model calibration, validation, and testing and the locality of the model developed—the majority need a re-parameterization from lake to lake. An Unmanned aerial vehicle (UAV) data-based model for chl_a estimation is developed in this work and tested on Sentinel-2 imagery without any re-parametrization. The Ensemble-based system (EBS) algorithm was used to train the model. The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R2 = Nash = 0.94 and RMSE = 5.6 µg chl_a L−1). A blind database (collected over 89 lakes) was used to challenge the EBS’ Sentine-2-derived chl_a estimates at a regional scale. Results were relatively less good, yet satisfactory (R2 = 0.85, RMSE= 2.4 µg chl_a L−1, and Nash = 0.79). However, the EBS has shown some failure to correctly retrieve chl_a concentration in highly turbid waterbodies. This particularity nonetheless does not affect EBS performance, since turbid waters can easily be pre-recognized and masked before the chl_a modeling.

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 Studies on Novel Anti-jamming Technique of Unmanned Aerial Vehicle Data Link

2008 ◽  
Vol 21 (2) ◽  
pp. 141-148 ◽  
Author(s):  
Huang Wenzhun ◽  
Wang Yongsheng ◽  
Ye Xiangyang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Data Link ◽  
Vehicle Data ◽  
Aerial Vehicle

scholarly journals Mechanical Harvesting Effectively Controls Young Typha spp. Invasion and Unmanned Aerial Vehicle Data Enhances Post-treatment Monitoring

2017 ◽  
Vol 8 ◽  
Author(s):  
Shane C. Lishawa ◽  
Brendan D. Carson ◽  
Jodi S. Brandt ◽  
Jason M. Tallant ◽  
Nicholas J. Reo ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Treatment Monitoring ◽  
Post Treatment ◽  
Vehicle Data ◽  
Aerial Vehicle

Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data

2020 ◽  
Vol 177 ◽  
pp. 105708 ◽  
Author(s):  
Tingting Chen ◽  
Weiguang Yang ◽  
Huajian Zhang ◽  
Bingyu Zhu ◽  
Ruier Zeng ◽  
...  
Keyword(s):  
Early Detection ◽  
Unmanned Aerial Vehicle ◽  
Bacterial Wilt ◽  
Vehicle Data ◽  
Leaf Level ◽  
Aerial Vehicle
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