scholarly journals Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing - An Application of Unmanned Aerial Vehicle and Field Investigation Data -

2016 ◽  
Vol 32 (5) ◽  
pp. 483-497
Author(s):  
Sang-il Na ◽  
Chan-won Park ◽  
Young-kuen Cheong ◽  
Chon-sik Kang ◽  
In-bae Choi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Indices ◽  
Field Investigation ◽  
Wheat Growth ◽  
Aerial Vehicle ◽  
Selection Of

Selection of Optimal Vegetation Indices for Predicting Winter Crop Dry Matter Based on Unmanned Aerial Vehicle

2020 ◽  
Vol 40 (4) ◽  
pp. 196-202
Author(s):  
Jae-Young Shin ◽  
◽  
Jun-Min Lee ◽  
Seung-Hak Yang ◽  
Kyoung-Jae Lim ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Dry Matter ◽  
Vegetation Indices ◽  
Aerial Vehicle ◽  
Winter Crop ◽  
Selection Of

Processing and Mosaicing System of Civil Unmanned Aerial Vehicle Remote Sensing Image

2014 ◽  
Vol 989-994 ◽  
pp. 3548-3551
Author(s):  
Ying Yang ◽  
Li Jun Wang ◽  
Lei Yang
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Control Points ◽  
Digital Cameras ◽  
Practical Application ◽  
Aerial Vehicle ◽  
Interactive Data ◽  
Low Altitude ◽  
Lower Magnitude ◽  
Selection Of

With combining Unmanned Aerial Vehicle (UAV) and the high-precision of the low altitude Remote Sensing (RS) technology, UAV RS technology has become an important compleme-nt for satellite and manned aircraft RS, so it has drawn great attention of people at home and abroad. Most of UAV RS image technologies use small digital cameras to shoot so that it causes some proble-ms such as lower magnitude, more and larger morphing image. The image processing and mosaicing system of UAV RS implements the function of image identifying, selection of image group control points, geometric correction, mosaicing, seam lines eliminating automatically and rapidly with Interactive Data Language (IDL), and is targeted to solve the practical application problems such as preprocessing, mosaicing, seam-lines removing of civil UAV RS image.

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

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.

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

scholarly journals Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 397
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Sustainable Management ◽  
Large Scale ◽  
Spectral Signature ◽  
Accurate Information ◽  
Data Interoperability ◽  
Critical Issues ◽  
Management Context ◽  
Aerial Vehicle

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV”+“forest”. This result is even more surprising when compared with similar research for “UAV”+“agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely,(1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

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