Journal of Applied Remote Sensing

Editor-in-Chief: Ni-Bin Chang, University of Central Florida

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications. 

Calls for Papers
How to Submit a Manuscript

Regular papers: Submissions of regular papers are always welcome.

Special section papers: Open calls for papers are listed below. A cover letter indicating that the submission is intended for a particular special section should be included with the paper.

To submit a paper, please prepare the manuscript according to the journal guidelines and use the online submission systemLeaving site. All papers will be peer‐reviewed in accordance with the journal's established policies and procedures. Authors have the choice to publish with open access.

Integrating New Sensing Techniques and Nondestructive Evaluation Systems for Smart Production with Industry 4.0 Standards
Publication Date
Vol. 15, Issue 1
Submission Deadline
Manuscripts due 1 May 2020.
Guest Editors
Sheng-Jen (“Tony”) Hsieh

Rockwell Automation Laboratory
College of Engineering
Texas A&M University
College Station, Texas, USA
hsieh@tamu.edu

Leonard J. Bond

Department of Aerospace Engineering
Department of Mechanical Engineering
Iowa State University
Ames, Iowa, USA
bondlj@iastate.edu

Xiaoyan Han

Infrared Imaging and Materials Characterization Laboratory
College of Engineering
Wayne State University
Detroit, Michigan, USA
xiaoyan.han@wayne.edu

Scope

Advances in Industrial Internet of Things (IIoT), cloud computing, robotics, and big data analytics have enabled the transformation of data into knowledge that can be used to monitor, control, and assess processes, systems, and services in real-time with high accuracy. The incorporation of smart sensory devices and wireless network architectures into these systems is known as Industry 4.0. 

Nondestructive evaluation (NDE) concepts-such as sensing and analysis techniques to examine and ensure the reliability and quality of the materials, products, processes, and systems-have been implemented in industry and services. NDE typically requires a long and tedious setup for each inspection. More importantly, to enable NDE to work accurately in some inspection scenarios, breakthroughs such as new sensing and measurements techniques are needed. Industry 4.0 can provide solutions for these challenges, resulting in smart inspection, real-time and permanent monitoring and evaluation, and NDE 2.0. The Journal of Applied Remote Sensing will publish a special section on integrating new sensing techniques and nondestructive evaluation systems for smart production with Industry 4.0 standards. Potential topics include but are not limited to the following:

  • Innovative sensing and measurement techniques for materials, process, product, or system monitoring and evaluation
  • Case studies and/or showcase of novel sensing techniques and Industry 4.0 in smart inspection or production
  • Novel algorithms (including machine learning and deep learning) and devices such as Industrial Internet of Things (IIoT) for sensing, measurement or evaluation in production environments
  • Multispectral sensing systems and algorithms for smart production
  • Integrated sensor networks and cloud computing for real-time data mining in quality and production control
  • Perspectives and future directions on nondestructive evaluation sensing and analysis techniques (NDE 2.0)

To submit a manuscript for consideration, please prepare the paper according to JARS guidelines and submit via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included. Papers will be peer reviewed in accordance with the journal's established policies and procedures.

Smart Production
Advances in Remote Sensing for Forest Structure and Functions
Publication Date
Vol. 14, Issue 2
Submission Deadline
Closed to submissions.
Guest Editors
Lin (Tony) Cao

Department of Forest Resources Management
Faculty of Forestry
Nanjing Forestry University
Nanjing, China
lincao@njfu.edu.cn

Yunsheng Wang

Centre of Excellence in Laser Scanning Research
Finnish Geospatial Research Institute FGI
Masala, Finland
yunsheng.wang@nls.fi

Hao Tang

University of Maryland College Park
Department of Geographical Sciences
College Park, Maryland, USA
htang@umd.edu

Scope

Forest structure plays a key role in understanding ecosystem processes and functions as well as managing forest resources and carbon storage. In the last decade, significant progress has been witnessed in methodological development for extracting forest structure information, especially in finding links between forest structure and functions from spaceborne, airborne, unmanned aerial vehicle (UAV), and terrestrial mobile and stationary remote sensing.

Remote sensing data such as light detection and ranging (LiDAR) and digital images or the combination of these datasets continue to be extensively used to enhance the capability for providing information on forest structure and function in different applications. On the plot-level, forest structure measurement is experiencing a significant change resulting from the increasingly available point clouds from single-/multispectral LiDAR scanners, or advanced structure from motion (SfM) techniques with high spatial and spectral resolution images, that are mounted on various aerial and terrestrial platforms. On the tree-level, forest structure information can be extracted through delineating and three-dimensional modeling of individual trees from the point clouds to a high level of detail that includes the branches and leaves, informing our understanding of key forest attributes in fine details. This special section of the Journal of Applied Remote Sensing addresses the advancement of these technologies, specifically of forest structure and function for forestry applications. We encourage papers in the application of LiDAR and image-based point clouds from airborne, UAV, and terrestrial devices such as backpack, handheld, and stationary devices. This section includes but is not limited to the following potential topics:  

  • Utilizing conventional, full-waveform or multispectral airborne LiDAR data in extracting stand- and tree-level metrics (e.g., Lorey's mean height, canopy cover, effective LAI, forest yield, and aboveground biomass etc.).   
  • Assessing consistencies of UAV, backpack, hand-held, and conventional terrestrial LiDAR as well as image-based point clouds for estimating stand- and tree-level metrics (e.g., tree height, DBH, stem density, crown dimension, tree volume, biomass components, and growth etc.).  
  • Exploring ultra-high spatial and multispectral resolution UAV data to improve forest structure mapping capacity (e.g., species classification, phenology, and other biophysical attributes etc.).  
  • Integrating with conventional satellite datasets (e.g., Landsat, Sentinel series) for large-scale or long-term forest mapping.

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures.

Forest Structure
Instrument Calibration and Product Validation of GOES-R
Publication Date
Vol. 14, Issue 3
Submission Deadline
Manuscripts due 29 February 2020.
Guest Editors
Xiangqian Wu

National Oceanic and Atmospheric Administration (NOAA)
National Satellite, Data, and Information Service
Center for Satellite Applications and Research
5830 University Research Court
College Park, Maryland, 20740, USA
Xiangqian.Wu@noaa.gov

Changyong Cao

National Oceanic and Atmospheric Administration (NOAA)
National Satellite, Data, and Information Service
Center for Satellite Applications and Research
5830 University Research Court
College Park, Maryland, 20740, USA
Changyong.Cao@noaa.gov

Satya Kalluri

National Oceanic and Atmospheric Administration (NOAA)
National Satellite, Data, and Information Service
Center for Satellite Applications and Research
5830 University Research Court
College Park, Maryland, 20740, USA
Satya.Kalluri@noaa.gov

Jaime Daniels

National Oceanic and Atmospheric Administration (NOAA)
National Satellite, Data, and Information Service
Center for Satellite Applications and Research
5830 University Research Court
College Park, Maryland, 20740, USA
Jaime.Daniels@noaa.gov

Scope

The R-Series of Geostationary Operational Environmental Satellite (GOES-R) is a new generation of satellite that the National Oceanic and Atmospheric Administration (NOAA) operates to provide continuous surveillance of the land, ocean, and atmosphere of the United States of America and its environment. GOES-R was launched in November 2016. It became operational as GOES-16 in December 2017, supporting NOAA’s GOES-EAST mission. GOES-S was launched in March 2018. It will become operational as GOES-17 in January 2019, supporting NOAA’s GOES-WEST mission. With the successful deployment of GOES-R instruments and dissemination of its products, the Journal of Applied Remote Sensing will devote a special section to document the instrument calibration and product validation of GOES-R sensors. Calibration of all instruments onboard the GOES-R are welcome, including Advanced Baseline Imager (ABI), Extreme Ultraviolet and X-ray Irradiance Sensor (EXIS), Geostationary Lightning Mapper (GLM), Magnetometer (MAG), Space Environment In-Situ Suite (SEISS), and Solar Ultraviolet Imager (SUVI). Validation of all GOES-R products are welcome, including Level 1b, Level 2, and beyond. Relevant studies of similar instruments and products, for example the Advanced Himawari Imager (AHI) onboard the Japan Meteorological Agency (JMA) satellites and the Advanced Meteorological Imager (AMI) onboard the Korea Meteorological Agency (KMA), are also encouraged to submit.

To submit to this special section, please prepare the paper according to JARS guidelines and submit via the online submission system (https://jars.msubmit.net). Cover letter should indicate that the submission is intended for this special section. Papers in this special section have the option of publishing with open access. Papers will be peer reviewed in accordance with the journal’s established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Instrument Calibration and Product Validation
Representation Learning and Big Data Analytics for Remote Sensing
Publication Date
Vol. 14, Issue 3
Submission Deadline
Closed to submissions.
Guest Editors
Weifeng Liu

China University of Petroleum (East China)
College of Information and Control Engineering
Qingdao, China
liuwf@upc.edu.cn

Yicong Zhou

University of Macau
Department of Computer and Information Science
Macao, China
yicongzhou@um.edu.mo

Karen Panetta

Tufts University
Department of Electrical and Computer Engineering
Medford, Massachusetts, USA
karen@ece.tufts.edu 

Sos Agaian

City University of New York
College of Staten Island
New York, New York, USA
sos.agaian@csi.cuny.edu 

Scope

With the rapid growth of remote sensing data delivered by remote sensors, it has become increasingly critical to explore efficient and effective artificial intelligence methodologies and data science techniques for complex information retrieval in remote sensing. Although many promising achievements were reported for signal and image processing applications, it is still a great challenge to develop distinctive algorithms for unique remote sensing analytics.

Recently, representation or feature learning has drawn wide public attention and plays an important role for signal and image analysis. There are some successful examples of representation learning. For example, graph convolutional networks have dramatically improved the performance of image classification; self-representation learning methods have been applied for segmentation and clustering; spatial convolution related methods have been successfully applied to semantic segmentation and region recognition; and long short-term memory networks have been successfully used in relation extraction and multi-hop reading comprehension.

This special section aims to demonstrate the contribution of representation learning algorithms to the research of remote sensing analytics, including learning models and algorithms, deep neural networks, and graph convolutional networks.

The editors expect to collect a set of recent progress in related topics to provide a forum for researchers to exchange their innovative ideas on representation learning solutions for remote sensing analytics, and to bring in interesting explorations of learning algorithms for particular remote sensing applications.

In summary, this special section welcomes a broad range of submissions that report novel representation learning techniques for remote sensing analytics. We are especially interested in: (1) theoretical advancements as well as algorithmic developments for representation learning connected with particular remote sensing analytics problems, (2) reports of practical applications and system innovations in remote sensing, and (3) novel big data sets as test beds for new developments, preferably with implemented standard benchmarks. Topics of interest include but are not limited to:

  • Spatial, temporal, and spectral representation learning models for remote sensing analytics
  • Spatial, temporal, and spectral convolution models for remote sensing analytics
  • Feature extraction/learning for remote sensing images
  • Recurrent neural networks for remote sensing analytics
  • Reinforcement learning for remote sensing analytics
  • Other applications of representation learning algorithms for band selecting/segmentation/target detection in remote sensing
  • Integrative representation learning and big data analytics for multisensor and multi-scale applications

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

 

Representation Learning
Impact Assessment of Climatic Change on Water Resource Management: A Remote Sensing Approach
Publication Date
Vol. 14, Issue 4
Submission Deadline
Manuscripts due 15 January 2020.
Guest Editors
Cyrus Samimi

Universität Bayreuth
Faculty of Biology, Chemistry and Earth Sciences Climatology
Bayreuth, Germany
cyrus.samimi@univ-bayreuth.de

Hayley J. Fowler

Newcastle University
School of Civil Engineering and Geosciences
Newcastle upon Tyne, United Kingdom
h.j.fowler@ncl-ac.uk

Paul L. G. Vlek

University of Bonn
Centre for Development Research
Bonn, Germany
p.vlek@univ-bonn.de

Scope

Climatic changes directly impact water-based resources, dynamically affecting the overall hydrological cycle on the Earth’s surface. It has the possibility of creating drastic changes in the water resource-based sectors, which includes reduction/increase in flood, water navigation channels, coastal sustainability, water availability, quality of water bodies, hydropower, etc. Due to unpredictable climatological impact, the hydrological cycle around the world is being affected, resulting in devastating changes and risks, such as rapid melting of glaciers, inaccurate prediction of rainfall and cyclones, increase in water scarcity, sea level rise, decrease in freshwater, changing groundwater level, and many others. However, to vanquish these intensive problems, advanced remote sensing techniques may possess a great potential in understanding the environmental crisis in a better way. Identifying the various global changes over the surface of the Earth at a large scale and deepening our insights in this regard have become indispensable with the aid of remote sensing. It is believed that modern remote sensing techniques will help resolve the challenges of the changing environment and support sustainable water resource management by providing appropriate measures at scales that are to be taken in transient and future climatic conditions.

For instance, robust planning and careful design of remote sensing techniques can help carry out long-term water resource management strategies and aid in holistic decision-making. Flood routing and storage, reliable production of hydropower, organized distribution of irrigation water, reclamation for waste water effluent, water quality monitoring and prevention of diseases, recharging of ground water, and management of environmental flows, which can be implemented by different public or private entities. Remote sensing techniques can even integrate the impact assessment of flood, drought and water-borne disease at local, regional, or even a global scale.

However, numerous bio-geophysical and bio-geochemical driving factors and socio-environmental aspects have to be taken into account simultaneously in maintaining water resource management strategies under extreme weather conditions. Yet availability of accurate climate information is deemed important for decision-making. This special section will focus on research work that evaluates and demonstrates various new models, methods, techniques, and assessments with advancement of remote sensing science and technology for water resource management studies under climate change.

Authors may submit papers on multidisciplinary topics including but not limited to:

  • Remote sensing studies on coastal water quality monitoring and coastal sustainability assessment with data fusion or merging techniques
  • Synthesis of existing remote sensing products for entailing various hydrologic complexity
  • Integrated satellite remote sensing for flood and drought monitoring using microwave and/or optical remote sensing
  • Recent trends in remote sensing and data science for climate change assessment
  • Development of multifaceted strategies with remote sensing and machine learning techniques for addressing the effects of climate change
  • Remote sensing-based parameter estimation and detection for water quality and ecosystem assessment
  • An automated interpretation technique for surface water mapping
  • Opportunities and challenges in implementing remote sensing observations collected from multi-scale platforms/sensors
  • Remote sensing for water level fluctuation trends at varying scales
  • Role of snow and ice in global water conservation and management with respect to climatic conditions
  • Water resources based learning models with the aid of remote sensing analytics

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

 

Climatic change
Published Special Sections

CubeSats and NanoSats for Remote Sensing (July-September 2019)
Guest Editors: Thomas Pagano and Charles Norton

Advances in Deep Learning for Hyperspectral Image Analysis and Classification (April-June 2019)
Guest Editors: Masoumeh Zareapoor, Jinchang Ren, Huiyu Zhou, and Wankou Yang

Advances in Remote Sensing for Air Quality Management  (October-December 2018)
Guest Editors: Barry Gross, Klaus Schäfer, Philippe Keckhut

Advances in Agro-Hydrological Remote Sensing for Water Resources Conservation (October-December 2018)
Guest Editors: Antonino Maltese and Christopher M. U. Neale

Optics in Atmospheric Propagation and Adaptive Systems
(October-December 2018)
Guest Editors: Karin U. Stein, Szymon Gladysz, Christian Eisele, Vladimir P. Lukin

Recent Advances in Earth Observation Technologies for Agrometeorology and Agroclimatology (April-June 2018)
Guest Editors: Shi-bo Fang, George P. Petropoulos, and Davide Cammarano

Improved Intercalibration of Earth Observation Data (January-March 2018)
Guest Editors: Craig Coburn and Aaron Gerace

Feature and Deep Learning in Remote Sensing Applications (October-December 2017)
Guest Editors: John E. Ball, Derek T. Anderson, Chee Seng Chan

Recent Advances in Geophysical Sensing of the Ocean: Remote and In Situ Methods (July-September 2017)
Guest Editors: Weilin Hou and Robert Arnone

Remote Sensing for Investigating the Coupled Biogeophysical and Biogeochemical Process of Harmful Algal Blooms (January-March 2017)
Guest Editors: Alan Weidemann and Ni-Bin Chang

Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis (October-December 2016)
Guest Editors: Xin Huang, Paolo Gamba, and Bormin Huang

Advances in Remote Sensing for Renewable Energy Development: Challenges and Perspectives (2015)
Guest Editors: Yuyu Zhou, Lalit Kumar, and Warren Mabee

Onboard Compression and Processing for Space Data Systems (2015)
Guest Editors: Enrico Magli and Raffaele Vitulli

Management and Analytics of Remotely Sensed Big Data (2015)
Guest Editors: Liangpei Zhang, Qian (Jenny) Du, and Mihai Datcu

Remote Sensing and Sensor Networks for Promoting Agro-Geoinformatics (2014 and 2015)
Guest Editors: Liping Di and Zhengwei Yang

High-Performance Computing in Applied Remote Sensing: Part 3 (2014)
Guest Editors: Bormin Huang, Jiaji Wu, and Yang-Lang Chang

Airborne Hyperspectral Remote Sensing of Urban Environments (2014)
Guest Editors: Qian (Jenny) Du and Paolo Gamba

Progress in Snow Remote Sensing (2014)
Guest Editors: Hongjie Xie, Chunlin Huang, and Tiangang Liang

Advances in Infrared Remote Sensing and Instrumentation (2014)
Guest Editors: Marija Strojnik and Gonzalo Paez

Earth Observation for Global Environmental Change (2014)
Guest Editor: Huadong Guo

Advances in Onboard Payload Data Compression (2013)
Guest Editors: Enrico Magli and Raffaele Vitulli

Advances in Remote Sensing Applications for Locust Habitat Monitoring and Management (2013)
Guest Editors: Ramesh Sivanpillai and Alexandre V. Latchininsky

High-Performance Computing in Applied Remote Sensing: Part 2 (2012)
Guest Editors: Bormin Huang and Antonio Plaza

Advances in Remote Sensing for Monitoring Global Environmental Changes (2012)
Guest Editors: Yuyu Zhou, Qihao Weng, Ni-Bin Chang

High-Performance Computing in Applied Remote Sensing: Part 1 (2011)
Guest Editors: Bormin Huang and Antonio Plaza

Satellite Data Compression (2010)
Guest Editor: Bormin Huang

Remote Sensing for Coupled Natural Systems and Built Environments (2010)
Guest Editor: Ni-Bin Chang

Remote Sensing Applications to Wildland Fire Research in the Eastern United States: Selected Papers from the 2007 EastFIRE Conference - Part 2 (2009)
Guest Editors: John J. Qu and Stephen D. Ambrose

Remote Sensing of the Wenchuan Earthquake (2009)
Guest Editor: Huadong Guo

Remote Sensing Applications to Wildland Fire Research in the Eastern United States: Selected Papers from the 2007 EastFIRE Conference (2008)
Guest Editors: John J. Qu and Stephen D. Ambrose

Aquatic Remote Sensing Applications in Environmental Monitoring and Management (2007)
Guest Editors: Vittorio E. Brando and Stuart Phinn

Back to Top