scholarly journals Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland

2016 ◽  
Vol 8 (11) ◽  
pp. 968 ◽  
Author(s):  
Daud Kachamba ◽  
Hans Ørka ◽  
Terje Gobakken ◽  
Tron Eid ◽  
Weston Mwase
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Biomass Estimation ◽  
3D Data ◽  
Aerial Vehicle ◽  
Tropical Woodland

scholarly journals Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”

2020 ◽  
Vol 12 (6) ◽  
pp. 940 ◽  
Author(s):  
Xiuliang Jin ◽  
Zhenhai Li ◽  
Clement Atzberger
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Crop Production ◽  
Unmanned Ground Vehicle ◽  
Special Issue ◽  
Climate Conditions ◽  
Ground Vehicle ◽  
Global Food Security ◽  
3D Data ◽  
Potential Applications ◽  
Aerial Vehicle

High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.

scholarly journals New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (24) ◽  
pp. 4170
Author(s):  
Pengfei Chen ◽  
Fangyong Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Estimation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Green Band ◽  
Validation Data ◽  
Texture Parameters ◽  
Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

Scots pine stands biomass assessment using 3D data from unmanned aerial vehicle imagery in the Chernobyl Exclusion Zone

2021 ◽  
Vol 295 ◽  
pp. 113319
Author(s):  
Dmytrii Holiaka ◽  
Hiroaki Kato ◽  
Vasyl Yoschenko ◽  
Yuichi Onda ◽  
Yasunori Igarashi ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Scots Pine ◽  
Exclusion Zone ◽  
3D Data ◽  
Biomass Assessment ◽  
Aerial Vehicle ◽  
Pine Stands ◽  
Scots Pine Stands

scholarly journals Above Ground Biomass Estimation of Syzygium aromaticum using structure from motion (SfM) derived from Unmanned Aerial Vehicle in Paninggahan Agroforest Area, West Sumatra

2021 ◽  
Vol 9 (1) ◽  
pp. 39
Author(s):  
Try Surya Harapan ◽  
Ahsanul Husna ◽  
Thoriq Alfath Febriamansyah ◽  
Mahdi Mutashim ◽  
Andri Saputra ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Image ◽  
Biomass Estimation ◽  
Syzygium Aromaticum ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
West Sumatra ◽  
Aerial Vehicle ◽  
The Difference ◽  
The Individual

Above ground biomass (AGB) is all living organic matters above the soil including stem, seed and leaves. This study aimed to estimate the individual clove (Syzygium aromaticum) and it’s above ground biomass using Unmanned Aerial Vehicle in the Agroforestry area in Paninggahan, West Sumatra. This study used a photogrammetry method to calculate trees and estimated the AGB. We detected 257 numbers of trees based on aerial image analysis and observed 270 after we validated on ground check in the field. The result was slightly different between estimated AGB from UAV and observed AGB from our ground validation. The estimated AGB was 5.9 ton/ Ha where the surveyed AGB was 5.6 ton/Ha. The difference between estimated AGB and observed AGB was 0.3 ton/Ha.

3D Data Fusion Using Unmanned Aerial Vehicle (UAV) Photogrammetry and Terrestrial Laser Scanner (TLS)

2018 ◽  
pp. 295-305 ◽  
Author(s):  
Mohamad Aizat Asyraff Mohamad Azmi ◽  
Mohd Azwan Abbas ◽  
Khairulazhar Zainuddin ◽  
Mohamad Asrul Mustafar ◽  
Mohd Zainee Zainal ◽  
...  
Keyword(s):  
Data Fusion ◽  
Unmanned Aerial Vehicle ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
3D Data ◽  
Aerial Vehicle

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

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

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

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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