Journal of Applied Remote Sensing

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

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. 

On the cover: The figure is from the paper “Efficient and de-shadowing approach for multiple vehicle tracking in aerial video via image segmentation and local region matching” by X. Zhang and X. Zhu in Vol. 14, Issue 1.

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.

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
Closed to submissions.
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
Improving Agricultural Productivity to Meet Increased Food Demands using Remote Sensing
Publication Date
Vol. 15, Issue 2
Submission Deadline
1 August 2020
Guest Editors
Oscar Sanjuán Martínez

Universidad Internacional de la Rioja
Logroño, Spain
Oscar.sanjuan@unir.net

Giuseppe Fenza

Department of Business Sciences - Management & Innovation Systems
University of Salerno
Fisciano SA, Italy
gfenza@unisa.it

Ruben Gonzalez Crespo

Universidad Internacional de la Rioja
Department of Engineering, School of Engineering and Technology
Logroño, Spain
ruben.gonzalez@unir.net

Scope

Since ancient times, agriculture has played a vital role in a country’s growth and forms the lifeline of the global economy. Worldwide, agriculture has brought in progressive changes across the production and manufacturing sectors. The constant evolution of modern technologies has introduced various advancements in agriculture and has led to the establishment of scientific farming methodologies. Production in agriculture greatly influences the rise and fall of the country’s economy and determines the success of the country in the global market. This phenomenon is specifically veracious for developing countries with increasing population measures. This is because growing population results in increased food demands with a requirement for increased agriculture productivity measures. The rise of population in a global context with lesser agricultural productivity can often result in global food demands. Achieving the goal of higher productivity is difficult, as the current agricultural sector struggles with changing climatic conditions and natural disasters.

Remote sensing in agriculture has significant benefits that help with the acquisition of agricultural field data or other significant phenomena without any physical contact with the object. It provides a timely and accurate picture of the agricultural regions with high revisit frequency measures. The application of remote sensing in agriculture minimizes environmental impacts over crops and maximizes productivity measures with earlier detection and prevention of environmental hazards and deadly diseases. Some of the recent research on remote sensing for agriculture includes forecasting crop production rates, horticulture, crop identification, cropping system analysis, crop progress, and damage assessment, pest identification and disease prevention measures, soil mapping, drought monitoring, land cover and degradation mapping, crop nutrition deficiency monitoring, crop yield forecasting, etc. In an agro-based remote sensing environment, the sensors are far from the agricultural object being observed; the input data is captured from the agricultural fields and transmitted to the destination through physical carriers, such as electromagnetic radiation. The output is usually produced in the form of an image that represents the captured objects. Some of the considerable advancements in further analysis and interpretation of the sensed data with improved remote sensing techniques could significantly increase the growth of agricultural productivity. Further, the integration of remote sensing techniques with advanced computing technologies, such as the Internet of Things (IoT), artificial intelligence, deep learning, and federated learning, could enhance yield productivity and efficiently fulfill global food demands.   

The Special Section  on Improving Agricultural Productivity to Meet Increased Food Demands using Remote Sensing aims to increase global agricultural productivity with advanced remote sensing techniques. The topics of the special section include but are not limited to the following:

  • Advancement in low spatial resolution sources for monitoring global environmental changes and agriculture
  • Earlier pest detection and control using improved spectral properties
  • Effective measures to enhance precision/information accuracy in remote-sensing techniques
  • Frontiers in remote sensing for agricultural productivity
  • Airborne and unmanned aerial vehicle remote sensing techniques for flexible monitoring of agricultural fields using different spatial scales
  • Innovative remote sensing operational techniques for agricultural field study using on-site and space data
  • Integrating new sensing techniques with advanced computational intelligence algorithms (deep learning, machine learning, federated learning, artificial intelligence) to improve crop yield across agricultural fields.
  • Effective ways of representation learning and big data analytics for remote sensing in agriculture
  • Remote sensing and IoT integrated approach to meet global food demands
  • Recent advancement in remote sensing techniques to detect land use and land covers for improving agriculture
  • Significance of hyperspectral remote sensing in agriculture and its future directions
  • Combined image processing approach (spatial and spectral) for remote sensing
  • Target and anomaly detection with image processing and remote sensing for precision agriculture

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.

Agricultural Productivity
Satellite Remote Sensing for Disaster Monitoring and Risk Assessment, Management, and Mitigation
Publication Date
Vol. 15, Issue 3
Submission Deadline
15 October 2020
Guest Editors
Hung Lung Allen Huang

University of Wisconsin-Madison
Space Science and Engineering Center/
Cooperative Institute for Meteorological
Satellite Studies
allenh@ssec.wisc.edu

Mitchell Goldberg

NOAA
Joint Polar Satellite System
mitch.goldberg@noaa.gov

Scope

Severe weather, hurricane, typhoon, heatwave, fire, flash flood, pollution, and volcano eruption are among the most destructive, frequent, and costly natural and man-made disasters endured by modern society, and they are expected to increase in severity and frequency that will greatly impact quality of life and commerce, and create long-lasting aftermath to climate change and civilization. Every year new record severe weather, hurricanes, fires, and floods are widely reported. The uncommon is becoming common; the unusual is turning to usual. The toll of these disaster events, in financial costs, displacement of individuals, and loss of properties and lives, are substantial and continue to rise as climate change and human-induced activities generate more extreme weather and environment-related disaster events.

According to the NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2020) survey (https://www.ncdc.noaa.gov/billions/). In 2019, there were 14 weather and climate disaster events with losses exceeding $1 billion each across the United States. These events included 3 flooding events, 8 severe storm events, 2 tropical cyclone events, and 1 wildfire event. Overall, these events resulted in the deaths of 44 people and had significant economic effects on the areas impacted. The 1980–2019 annual average is 6.5 events (CPI-adjusted); the annual average for the most recent 5 years (2015–2019) is 13.8 events (CPI-adjusted). That the annual average number of disaster events is on the rise is evident.

Satellite remote sensing byways of operational weather and environment satellite systems onboard Low Earth Orbits (LEOs) and Geostationary Earth Orbits (GEOs) using passive and active optical sensors are fully capable of detecting, quantifying and monitoring the location, intensity, and trend of these type disasters. The so-called “big-three” Earth observation agencies---the National Oceanic and Atmospheric Administration (NOAA) of USA, EUMETSAT of European Union (EU), and China Meteorological Administration (CMA) of China---are routinely operating such weather and environment observing systems continuously and globally. 

The disaster management community requires frequently updated and easily accessible information to better understand the extent of the disaster and better coordinate response efforts. With joint international agencies and community coordinated efforts under the World Meteorological Organization (WMO), this special section calls for papers with potential topics including but not limited to the following:

  • Remote sensing sensor technology suitable for observing weather and environmental disasters;
  • Timely and accurate processing algorithm that can detect, quantify, and monitor disaster events;
  • Product fusion and integration to enhance accuracy and provide reliable disaster information;
  • Low latency information dissemination mechanism and infrastructure that can meet the challenges of real-time disaster assessment and management;
  • Interactive visualization system capable of managing multiple disaster information for real-time decision making;
  • Cloud-based service enterprise system for routine operational support;
  • The use of satellite direct broadcast receiving and retransmission system to meet low latency information dissemination challenge and
  • other innovations that effectively address the evolving needs for monitoring and mitigate all types of disaster events observed from space-based remote sensing vantage points. 

In summary, this special section welcomes a broad range of submissions that report operational, research, commercial, and novel approaches for effective use of satellite remote sensing information for disaster monitoring, risk assessment, management, and mitigation.

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.

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

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