Applicability of unmanned aerial vehicle for chlorophyll-a map in river

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.

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Hybrid Approach of Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Assessment of Chlorophyll-a Imagery Using Spectral Indices in Stream, South Korea

Water ◽

10.3390/w13141930 ◽

2021 ◽

Vol 13 (14) ◽

pp. 1930

Author(s):

Eun-Ju Kim ◽

Sook-Hyun Nam ◽

Jae-Wuk Koo ◽

Tae-Mun Hwang

Keyword(s):

Chlorophyll A ◽

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Hybrid Approach ◽

Weather Conditions ◽

Field Application ◽

Spectral Indices ◽

Unmanned Surface Vehicle ◽

Aerial Vehicle

The purpose of this study is to compare the spectral indices for a two-dimensional river algae map using an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) hybrid system. The UAV and USV hybrid systems can overcome the limitation of not being able to effectively compare images of the same region obtained at different times and under different seasonal conditions, when using a method of comparing and analyzing with absolute values in remote sensing. Radiometric correction was performed to minimize the interference that could distort the analysis results of the UAV imagery, and the images were taken under weather conditions that would minimally affect them. Three spectral indices, namely, normalized difference vegetation index (NDVI), normalized green–red difference index (NGRDI), green normalized difference vegetation index (GNDVI), and normalized difference red edge index (NDRE) were compared for the chlorophyll-a images. In field application and correlational analysis, the NDVI was strongly correlated with chlorophyll-a (R2 = 0.88, p < 0.001), and the GNDVI was moderately correlated with chlorophyll-a (R2 = 0.74, p < 0.001). As a result of comparing the chlorophyll-a concentration with the in-situ chlorophyll-a imagery by UAV, we obtained the RMSE of NDVI at 2.25, and the RMSE of GNDVI at 3.41.

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Justification and selection of vegetation indices to determine the early soybeans readiness for harvesting

E3S Web of Conferences ◽

10.1051/e3sconf/202127301008 ◽

2021 ◽

Vol 273 ◽

pp. 01008

Author(s):

Rashid Kurbanov ◽

Natalia Zakharova

Keyword(s):

Chlorophyll A ◽

Unmanned Aerial Vehicle ◽

Vegetation Indices ◽

Broad Band ◽

Vehicle Monitoring ◽

Operational Information ◽

Aerial Vehicle ◽

Chlorophyll A And B ◽

Soybean Leaves ◽

Selection Of

An unmanned aerial vehicle monitoring provides operational information on soybean harvesting readiness for breeders and agronomists. The purpose of the study is to substantiate the choice of vegetation indices to assess the content of chlorophyll a and b, which contribute to determining the early readiness of soybean crops for harvesting, based on data obtained from an unmanned aerial vehicle. The research was conducted at the soybean breeding field in 2020. Seven broad-band vegetation indices NDVI, NDRE, ClGreen (GCI), CCCI, CVI, GNDVI, MCARI were used in the study, estimating changes in chlorophyll a and b content in soybean leaves. Vegetation indices, which in complex testify to the early readiness of soybean crops for harvesting, have been revealed. The statistical difference between the values of the indices of early maturing and late maturing samples was calculated. The data obtained contributed to the development of custom formulas for assessing the early harvesting readiness of soybeans.

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Cooperative Navigation between a Ground Vehicle and an Unmanned Aerial Vehicle in GNSS-Challenged Environments

Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016) ◽

10.33012/2016.14623 ◽

2016 ◽

Author(s):

Victor O. Sivaneri ◽

Jason N. Gross

Keyword(s):

Unmanned Aerial Vehicle ◽

Ground Vehicle ◽

Cooperative Navigation ◽

Aerial Vehicle

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Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

Journal of Applied Reliability ◽

10.33162/jar.2020.12.20.4.332 ◽

2020 ◽

Vol 20 (4) ◽

pp. 332-342

Author(s):

Hyung Jun Park ◽

Seong Hee Cho ◽

Kyung-Hwan Jang ◽

Jin-Woon Seol ◽

Byung-Gi Kwon ◽

...

Keyword(s):

Fault Diagnosis ◽

Unmanned Aerial Vehicle ◽

Sensor Data ◽

Planetary Gearbox ◽

Aerial Vehicle

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Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

Light & Engineering ◽

10.33383/2017-061 ◽

2018 ◽

pp. 7-13

Author(s):

Anton M. Mishchenko ◽

Sergei S. Rachkovsky ◽

Vladimir A. Smolin ◽

Igor V . Yakimenko

Keyword(s):

Spatial Structure ◽

Unmanned Aerial Vehicle ◽

Wavelength Range ◽

Spectral Range ◽

Near Ir ◽

Optoelectronic Systems ◽

Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

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