You are currently viewing a new version of our website. To view the old version click .

Remote Sensing, Volume 12, Issue 22

November-2 2020 - 172 articles

Cover Story: Employing red-green-blue (RGB) imagery acquired from UAV remote sensing to discriminate healthy from diseased plant areas and monitor the progress of such plant diseases in fields has yet to be fully investigated. Here, wheat leaf rust and stripe rust diseased leaf areas in winter wheat were identified and their severities quantified during the critical period for efficacious fungicide application using RGB imagery-derived indices. Good agreements between the UAV-based estimates and observations were found for both fungal diseases, with statistically significant correlations (P < 0.0001). The study provides clear evidence that UAV-based RGB imagery is a useful tool for monitoring fungal foliar diseases throughout the cropping season, supporting the identification of potential new disease outbreaks and efficient control of their spread. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
  • You may sign up for email alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.

Articles (172)

  • Article
  • Open Access
27 Citations
5,053 Views
18 Pages

Investigating the Long-Range Transport of Aerosol Plumes Following the Amazon Fires (August 2019): A Multi-Instrumental Approach from Ground-Based and Satellite Observations

  • Hassan Bencherif,
  • Nelson Bègue,
  • Damaris Kirsch Pinheiro,
  • David Jean du Preez,
  • Jean-Maurice Cadet,
  • Fábio Juliano da Silva Lopes,
  • Lerato Shikwambana,
  • Eduardo Landulfo,
  • Thomas Vescovini and
  • Casper Labuschagne
  • + 7 authors

23 November 2020

Despite a number of studies on biomass burning (BB) emissions in the atmosphere, observation of the associated aerosols and pollutants requires continuous efforts. Brazil, and more broadly Latin America, is one of the most important seasonal sources...

  • Letter
  • Open Access
14 Citations
4,071 Views
14 Pages

Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation

  • Carlos García Rodríguez,
  • Jordi Vitrià and
  • Oscar Mora

23 November 2020

In recent years, different deep learning techniques were applied to segment aerial and satellite images. Nevertheless, state of the art techniques for land cover segmentation does not provide accurate results to be used in real applications. This is...

  • Article
  • Open Access
15 Citations
3,917 Views
17 Pages

Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach

  • Kyubyung Kang,
  • Donghui Chen,
  • Cheng Peng,
  • Dan Koo,
  • Taewook Kang and
  • Jonghoon Kim

23 November 2020

Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challen...

  • Article
  • Open Access
3 Citations
3,103 Views
22 Pages

23 November 2020

The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ra...

  • Article
  • Open Access
25 Citations
4,658 Views
35 Pages

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

  • Vladimir Lukin,
  • Irina Vasilyeva,
  • Sergey Krivenko,
  • Fangfang Li,
  • Sergey Abramov,
  • Oleksii Rubel,
  • Benoit Vozel,
  • Kacem Chehdi and
  • Karen Egiazarian

23 November 2020

Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression sho...

  • Article
  • Open Access
14 Citations
3,675 Views
23 Pages

23 November 2020

Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to th...

  • Article
  • Open Access
6 Citations
2,811 Views
17 Pages

23 November 2020

Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advan...

  • Article
  • Open Access
7 Citations
3,159 Views
14 Pages

Characterization of Intertidal Bar Morphodynamics Using a Bi-Annual LiDAR Dataset

  • Anne-Lise Montreuil,
  • Robrecht Moelans,
  • Rik Houthuys,
  • Patrick Bogaert and
  • Margaret Chen

23 November 2020

Intertidal bars are common features on meso-and macro-tidal sandy beaches with low to moderate wave energy environments. Understanding their morphodynamics is, hence, crucial for enhancing our knowledge on beach processes which is beneficial for coas...

  • Article
  • Open Access
23 Citations
7,090 Views
25 Pages

23 November 2020

Rapid urbanization processes and indiscriminate disposal of urban wastewaters are major causes for anthropogenic lake-sediment deposition and eutrophication. However, information about the spatial and temporal variation of macrophyte and phytoplankto...

  • Article
  • Open Access
23 Citations
4,991 Views
20 Pages

23 November 2020

Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate tra...

of 18

Get Alerted

Add your email address to receive forthcoming issues of this journal.

XFacebookLinkedIn
Remote Sens. - ISSN 2072-4292