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 "Toward high-performance SPAD arrays for space-based atmosphere and ocean profiling LiDARs" by Giulia Acconcia et al. in Vol. 15, 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.

Satellite Remote Sensing for Disaster Monitoring and Risk Assessment, Management, and Mitigation
Publication Date
Vol. 15, Issue 3
Submission Deadline
Closed
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.

Hyperspectral Remote Sensing and Imaging Spectrometer Design
Publication Date
Vol. 15, Issue 3
Submission Deadline
Closed
Guest Editors
Shen-En Qian

Optical Payloads
Space Science and Technology
Canadian Space Agency
Saint-Hubert, Québec, Canada
shen-en.qian@canada.ca

Robert O. Green

AVIRIS and Imaging Spectroscopy
Earth Venture EMIT
Jet Propulsion Laboratory
Pasadena, California, USA
robert.o.green@jpl.nasa.gov

Antonio J. Plaza

Hyperspectral Computing Lab
Department of Technology of Computers & Communication
University of Extremadura
Cáceres, Spain
aplaza@unex.es

Scope

Hyperspectral imaging has emerged as a new generation of remote sensing technology since the late 1980s when the first airborne hyperspectral instrument, AVIRIS, became operational, followed by the launch of the first spaceborne hyperspectral sensor, Hyperion, onboard NASA’s EO-1 satellite in 2000. The detailed spectral information provided by hyperspectral imagery and the spatial relationships among the different spectra in a neighborhood often provide results not possible with multispectral or other types of imagery. Hyperspectral imaging technology goes well beyond the traditional remote sensing applications ranging from agriculture, forestry, environmental monitoring, geology, defense, intelligence, and law enforcement to food safety and inspection, and medical imaging. Many imaging spectrometers have been designed and built for spaceborne, airborne, UAV/UAS, and ground-based systems. The R&D on processing techniques of the hyperspectral imaging and hyperspectral applications is one of the most hot areas in remote sensing community. Many papers on this topic have been published. The Journal of Applied Remote Sensing will publish a special section on hyperspectral remote sensing and imaging spectrometer design. Potential topics include but are not limited to the following:

Hyperspectral Sensor Design and Implementation

  • Design and development of imaging spectrometers for all spectral regions from the UV to the thermal IR, for spaceborne, airborne, UAV/UAS, and ground-based systems
  • Novel architectures and spectrometer designs
  • Verification and calibration methods and techniques
  • Simulation techniques in sensor design and characterization
  • Sensor artifact assessment and suppression
  • Enabling technologies

Hyperspectral Data Applications and Processing

  • Geology and mineralogy for earth and planetary applications
  • Vegetation monitoring and health assessment
  • Ocean, coastal ocean and inland waters
  • Atmospheric sounding and imaging for greenhouse gas and air quality
  • Defense and homeland security
  • Emergency response, disaster recovery, and remediation
  • Data processing and exploitation methods and algorithms
  • Spectral signature libraries and databases
  • Radiative transfer modeling
  • Atmospheric correction techniques
  • Detection, classification and characterization
  • Sensor and data fusion,
  • Hyperspectral image sharpening and super-resolution
  • De-noising
  • Physics-based spectral phenomenology understanding and modeling
  • Deep learning or machine learning based approaches for spectral processing 

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.

Hyperspectral remote sensing ...
Satellite Hyperspectral Remote Sensing: Algorithms and Applications
Publication Date
Vol. 15, Issue 4
Submission Deadline
1 June 2021
Guest Editors
Kun Tan

Key Laboratory of Geographic Information Science (Ministry of Education)
East China Normal University
Shanghai, China
tankuncu@gmail.com

Xiuping Jia

University of New South Wales
School of Engineering and Information Technology
Canberra, Australia
x.jia@adfa.edu.au

Antonio J. Plaza

University of Extremadura
Hyperspectral Computing Lab
Department of Technology of Computers & Communication
Cáceres, Spain
aplaza@unex.es

Scope

Hyperspectral remote sensing is currently a fast-moving area of not only research but also remote sensing applications. The high spectral resolution in hyperspectral images and the spatial relationship between adjacent regions provide critical information that cannot be provided by multispectral or other types of images. Its ability to capture the physical and chemical properties of scene materials opens the way to a better understanding and monitoring of a large variety of land cover and land use. On the other hand, the high correlation between adjacent bands and increased data redundancy bring great challenges to the analysis of satellite hyperspectral images. Advanced computing techniques, such as artificial intelligence, deep learning, and weak-supervised learning, have been developed in recent years to overcome the deficiencies of hyperspectral images in space and time, and to enhance their applications.

In this special section, we aim to compile state-of-the-art research on the application of satellite hyperspectral remote sensing, such as GAOFEN-5, ZY1-02D, PRISMA, DESIS, HySIS etc. Potential topics include but are not limited to the following:

  • Land cover type classification
  • Hyperspectral remote sensing images unmixing
  • Object identification and anomaly detection
  • Atmospheric study
  • Environmental monitoring and pollution detection
  • Radiative transfer modeling
  • Vegetation monitoring and health assessment
  • Sensor and data fusion

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 six weeks of manuscript submission. The special section is 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.

Integration of Remote Sensing and Social Media
Publication Date
Vol. 15, Issue 4
Submission Deadline
15 May 2021
Guest Editors
Sadia Din

Kyungpook National University
School of Computer Science
South Korea
sadia.din@knu.ac.kr

Nauman Aslam

Northumbria University, Newcastle
Department of Computer and Information Sciences
United Kingdom
nauman.aslam@northumbria.ac.uk

Simon K. S. Cheung

The Open University of Hong Kong
Hong Kong, China
kscheung@ouhk.edu.hk

Scope

The rapid development of social media data and the associated growth in volume, velocity, and variety have grown exponentially in recent years. Data such as remote sensing satellite, GPS check-in records, vehicle location, aerial robotics, etc., can be used to guide traditional remote sensing image retrieval and information extraction tasks. Recently, with the popularity of internet and smart mobile devices, social media containing spatial information has evolved rapidly. Location-based social media data from sources such as Facebook, Weibo, Twitter, geolocated posts, and others are leading to new research areas, new technologies and methods, and new insights into urban observation. We are presented with a huge opportunity when we integrate and analyze remote sensing and social media data. Researchers can exploit those data to investigate the relationships among human, environment, and space. It can also help us manage disaster such as fire, rain, tsunami, and storm, etc., and monitor key parameters.

Social media plays an important role in our daily lives and offers a unique chance to gain valuable insights into information flow and social networks. Users can use the Instagram image-sharing service to attach their geographical coordinates to photos and share location information across social networks. Through data collection and content analysis, the information generated by social networks provides a new understanding of information sharing from a global perspective. Social media supplement the information provided by remote sensing.

 The main purpose of this special section is to provide a snapshot of the most recent advances and breakthroughs in the aforementioned research areas, with particular focus on the combination of remote sensing and social data. Papers are solicited pertaining to the following topics:

  • New algorithms and applications for the integration and joint analysis of remote sensing and social data in urban areas.
  • Joint exploitation of remote sensing and social media data for monitoring natural disasters.
  • New techniques and applications for fusion of remote sensing and social data.
  • Classification strategies based on the integration of remote sensing and social data.
  • Mapping and modelling of urban sprawl, urban climate, and urban heat islands using remote sensing and social data.
  • Efficient information processing and retrieval for urban area assessment using remote sensing and social data.
  • Digital repositories and databases integrating remote sensing and social data for different applications.
  • Parallel processing for computationally efficient analysis of remote sensing and social data.

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.

Multitemporal Remote Sensing Data Processing and Applications
Publication Date
Vol. 15, Issue 4
Submission Deadline
1 May 2021
Guest Editors
Liangpei Zhang

Wuhan University, China
zlp62@whu.edu.cn

Jocelyn Chanussot

Grenoble Institute of Technology, France
jocelyn.chanussot@grenoble-inp.fr

Assefa M. Melesse

Florida International University, USA
melessea@fiu.edu

Xinghua Li

Wuhan University, China
lixinghua5540@whu.edu.cn

Scope

As one of the most effective ways to observe the earth, remote sensing technology is widespread in our world. There are a variety of remote sensing platforms, e.g., ground, aerial and space, which carry series of optical, infrared, radar, and lidar sensors. No matter what the platform is, one of the things they have in common is the ability to provide multitemporal data. Multitemporal refers to the characteristics of a set of remote sensing images in different time series. Broadly speaking, a set of images, maps, or geographic data of the same region acquired at different times can be regarded as multitemporal data. In recent years, with the development of new remote sensing platforms and sensors, as well as the expansion of the scope of historical image data, research based on multitemporal data has become more and more popular in this field.

Multitemporal remote sensing data provide more characteristic information than single-temporal data. Therefore, they are of great value in image quality improvement, image interpretation, geoscience applications, and so on. At the same time, in the past decade, deep learning technology has developed rapidly and has achieved great success in many fields including multitemporal remote sensing applications.

The topics of this special section include but are not limited to the following:

  • Image quality improvement based on multitemporal data (e.g., image denoising, image super-resolution reconstruction, spatio-temporal fusion, multi-source data fusion, the detection and removal of cloud and shadow, image registration, radiometric correction, image restoration, etc.)
  • Information interpretation based on multitemporal data (e.g., ground object classification, change detection, anomaly detection, feature recognition, etc.)
  • Geoscience applications of multitemporal data (e.g., classification of land cover and land use, pollution detection of river, oil and air, disaster monitoring, land surface deformation monitoring, phenology monitoring, water bloom monitoring, etc.)
  • Time-series product generation (e.g., vegetation remote sensing products, hydrological remote sensing products, atmospheric remote sensing products, temperature and radiation products, ocean remote sensing products, etc.)
  • Deep learning based methods and applications using multitemporal data.

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.

Multitemporal
Adversarial Machine Learning and Explainable Artificial Intelligence in Remote Sensing
Publication Date
Vol. 16, Issue 1
Submission Deadline
1 July 2021
Guest Editors
Stanton R. Price

U.S. Army Engineer Research and Development Center
Geotechnical and Structures Laboratory
Vicksburg, Mississippi, USA
Stanton.R.Price@erdc.dren.mil

Derek T. Anderson

University of Missouri-Columbia
Department of Electrical Engineering and Computer Science
Columbia, Missouri, USA
andersondt@missouri.edu

Timothy C. Havens

Michigan Technological University
Department of Computer Science
Houghton, Michigan, USA
thavens@mtu.edu

Scope

As the artificial intelligence boom continues throughout countless applications and spans diverse fields, two emerging topics that are becoming increasingly important are adversarial machine learning (AML) and explainable artificial intelligence (XAI). These two topics, while on different ends of the research spectrum, have linkages in the core questions that are being answered. To develop effective AML algorithms, it can be beneficial to understand the mathematical underpinnings and architectural properties of whatever methodology is to be exploited. In areas like remote sensing, it can be difficult to obtain new data; AML provides a promising methodology for generating new data. XAI can be employed to increase the understandability of machine learning algorithms, be it in the form of mathematical interpretations, linguistic and visual descriptions, etc. In the foreseeable future, due to organizational, governmental, and industry standards, explanations are needed to enhance trust and aid in decision-making when deploying systems that utilize artificial intelligence. This special section considers AML, XAI, and, in particular, the intersection of these two topics in the context of remote sensing. Potential topics include but are not limited to the following:

Adversarial Machine Learning

  • Novel adversarial machine learning methods and techniques
  • Sensor specific and multi-sensor attacks
  • Advanced data poisoning methodologies
  • Novel black-box and white-box attack techniques
  • Human agent teaming for AML
  • Survey of recent AML applications, with an emphasis on remote sensing
  • Interesting case studies and applications in remote sensing
  • AML addressing broad spectrum analysis
  • Adversarial techniques spanning heterogeneous information sources

 Explainable Artificial Intelligence

  • Novel explainable artificial intelligence methodologies
  • Human-in-the-loop (HITL) XAI
  • Human-in- and over-the-loop actionable XAI
  • Identification of XAI guidelines for remote sensing applications
  • Articles focused on understandability, trustworthiness, accountability, and explainability for remote sensing
  • Methods based on visual, contextual, local explanations, and alike
  • Survey with a focus on XAI in remote sensing
  • XAI techniques spanning heterogeneous data sources

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 six 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.

Coastal Zone Remote Sensing for Environmental Sustainability
Publication Date
Vol. 16, Issue 1
Submission Deadline
31 July 2021
Guest Editors
Shuisen Chen

Guangzhou Institute of Geography
Guangdong Academy of Sciences
China
css@gdas.ac.cn

Chandrasekar Nainarpandian

Manonmaniam Sundaranar University, India
profncsekar@gmail.com


Scope

The ocean is the cradle of human civilization. As a transition region between land and sea, the coastal zone is the active exchange center of materials and energy, and also the strong coupling region of physical, chemical, biological, geological and other processes. Because of strong anthropogenic pressures, coastal zones are already suffering ecological and biological stresses, for example, poor water quality, pollution, and destruction of marine ecosystems. All kinds of natural and human activities seriously threaten the stability and sustainable development of the coastal zone. Pollution induced by human activities, coastal morphological changes due to ocean engineering and coastal erosion, land-use and cover change due to human activities are all hot topics in coastal zones environment.

Remote sensing technology, with its advantages of continually, rapidly, informatively and dynamically monitoring the large-scale environment, is one of the important means to obtain information such as resources, environment and disasters in the coastal zone. With the launch of numerous satellites, the increasingly mature UAV technology and the progress of sensor technology, the data about the coastal zone shows a geometric growth. At present, how to accurately perceive the dynamic information of the coastal zone from the massive remote sensing data is a very challenging subject.

Therefore, the development of remote sensing technology is an indivisible part of monitoring the coastal zone environment in the future. The purpose of this special issue to promote outstanding researches concerning this aspect in the realm of remote sensing technology for coastal zone environment, focusing on state-of-the-art progresses, developments and new treads.

Potential topics include but are not limited to the following:

  • Extraction of geographical elements including but not limited to land use type, coastline, intertidal zone using remote sensing data
  • Retrieval of ocean color parameters including but not limited to chlorophyll, colored dissolved organic matter, and suspended sediment using remote sensing data
  • Offshore water quality monitoring using remote sensing data
  • Surface temperature of coastal zone and its environmental effect analysis
  • Remote sensing monitoring of coastal wetlands and vegetations (such as mangroves, seagrass beds, coral reefs)
  • Application of UAV technology in the coastal zone
  • Application of remote sensing technology in ecological environment quality monitoring and evaluation
  • Application of remote sensing technology in material transport from land to ocean
  • The application of acoustic remote sensing in coastal zone environment monitoring
  • Lidar remote sensing in coastal regions

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 six 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.

Coastal Zones
Remote Sensing for Sustainable Forest Management
Publication Date
Vol. 16, Issue 1
Submission Deadline
15 July 2021
Guest Editors
Ali Ahmadian

University Mediterranea of Reggio Calabria
Reggio Calabria, Italy
ahmadian.hosseini@unirc.it

Valentina E. Balas

Aurel Vlaicu University
Arad, Romania
balas@drbalas.ro


Soheil Salahshour

Bahcesehir University
Istanbul, Turkey
soheil.salahshour@eng.bau.edu.tr


Scope

In the technologically developed world, remote sensing is potentially important for various fields, including engineering, forestry, geology, geography, urban planning, and agriculture. Also, forest inventory and management are rapidly changing due to current economic, environmental, and social policy objectives. Hence, using remote sensing techniques to acquire forest-related information for mapping, monitoring, and other scientific understanding is vital, since the main objective of forest management is productivity and conservation of resources for society’s needs. Implementing advanced remote sensing techniques for many purposes like biophysical parameter estimation, defoliation monitoring, carbon stock management, forest resources distribution mapping, invasive species mapping, and 3D structure measurement will enhance the sustainability of the forest. Moreover, remote sensing also observes climate and environmental changes for predicting and forecasting forest resources more effectively.

For effective forest resources management, new algorithms are being developed in virtual image analysis, which helps in retrieving accurate information from remote sensing. At the same time, advanced remote sensing technologies like airborne laser scanning, terrestrial laser scanning, digital aerial photogrammetry, and very-high-spatial-resolution optical imagery are valuable in analyzing forest inventories. Further, utilizing optical remote sensing technique has some specific challenges due to uncertain weather conditions. Therefore, to overcome this type of uncertain situation, advanced Doppler weather radar can be used along with numerical weather prediction models. Besides, remote sensing methods such as light detection and ranging (LiDAR) gives much more high-resolution images, and Geographic Information System (GIS) provides efficient geospatial information to manage forest inventory. Apart from the above-mentioned remote sensing techniques, recent developments in ground-based sensors also provides detailed information about forest resources, including advanced 3D measurements and community-based monitoring with cost effectiveness. Studies show that deforestation and forest degradation are major causes of greenhouse gas emission, which leads us to create awareness among the general population. As a consequence, certain countries implemented climate change mitigation programs like REDD to reduce emissions from deforestation and forest degradation. Since conserving forest and managing it is very important, integrating remote sensing and its related techniques will be a problem-solving measure for attaining sustainability in forest management.

Specific topics of interest include but are not limited to:

  • Recent advances in remote sensing applications for forest resource management
  • Comparison and evaluation of different remote sensor platforms for assessing forest ecology
  • Efficient real-time image processing for sustainable forestry
  • Remote sensing methods for assessment and monitoring of three-dimensional structure of forest
  • Remote sensing processing techniques in wildfire management
  • Application of remote sensing for tropical deciduous forests
  • Current advances and challenges of remote sensing for ecological balancing
  • Remote sensing techniques for mapping temperate rain forest regions
  • Implementing community-based monitoring for quality and management of natural resources
  • Remote sensing technology-based mitigation programs for deforestation and degradation
  • GIS based effective monitoring system for forest management
  • Remote sensing for monitoring structural and functional forest biodiversity
  • Applications of remote sensing in geoscience
  • LiDAR processing techniques for vegetation characterization

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 six 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.

Forest Management
Published Special Sections

Representation Learning and Big Data Analytics for Remote Sensing (July-September 2020)
Guest Editors: Weifeng Liu, Yicong Zhou, Karen Panetta, Sos Agaian

Instrument Calibration and Product Validation of GOES-R (July-September 2020)
Guest Editors: Xiangqian Wu, Changyong Cao, Satya Kalluri, Jaime Daniels

Advances in Remote Sensing for Forest Structure and Functions (April-June 2020)
Guest Editors: Lin (Tony) Cao, Yunsheng Wang, Hao Tang

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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