To submit a manuscript for consideration in a Special Section, please prepare the manuscript according to the journal guidelines and use the Online Submission System.
FORTHCOMING SPECIAL SECTIONS:
Advances in Remote Sensing Applications for Locust Habitat Monitoring and Management
High-Performance Computing in Applied Remote Sensing
Remote Sensing for Coupled Natural Systems and Built Environments
Advances in Remote Sensing Applications for Locust Habitat Monitoring and Management
Guest Editors:
Ramesh Sivanpillai
University of Wyoming
Department of Botany & WyGISC
1000 East University Avenue
Laramie, Wyoming 82071
Tel: +1-307-766-2721
E-mail: sivan@uwyo.edu
Alexandre V. Latchininsky
University of Wyoming
Department of Renewable Resources
1000 East University Avenue
Laramie, Wyoming 82071
Tel: +1-307-766-2298
E-mail: latchini@uwyo.edu
Call for Papers: Locusts are short-horned grasshoppers and are capable of forming swarms consisting of millions of individuals. These swarms can migrate long distances (200 km per day, thousands of kilometers during lifetime), destroying vegetation and adversely impacting the environment and livelihood of farming communities worldwide. It is estimated that one person in 10 world inhabitants can be affected by these pests. Given these enormous risks for food security, international and national agencies have implemented control measures aimed at preventing the formation of swarms and reducing their impact. For example, the desert locust outbreak in 2003–2005 affected 26 mostly African countries and an estimated USD 500 million was spent for controlling the locust populations and providing food aid to the impacted people. Monitoring locust habitats is an essential part of their population management program; however, most locust habitats cover vast geographic areas and some extend across countries and continents. Thus the desert locust recession area covers 16 million square km; the distribution area of the migratory locust is even larger, extending over four continents. These conditions make it suitable for the use of remote sensing technology for monitoring and mapping locust habitats. Several advances have been made in recent years with the availability of new types of remotely sensed data and methods developed for processing them. The goals of this special section of the Journal of Applied Remote Sensing (JARS) are to highlight the advances and the challenges in the use of remotely sensed data for mapping and monitoring different locust species habitats. Review and research papers are solicited in, but not limited to, the following areas:
• Current status and gaps in data collection and processing for generating locust habitat information
• Application of multispectral and active remote sensing data collected from aerial and satellite platforms
• Integration of remotely sensed data collected from multiple platforms and sensors
• Utility of vertical-looking RADAR and other types of remotely sensed data for tracking locust swarms
• Application of feature extraction and advanced image processing techniques
• Use of multitemporal remotely sensed data for monitoring and quantifying habitat changes
• Challenges in integrating near real-time remotely sensed data in a geographic information system (GIS)
• Integration of remote sensing, meteorological data, and locust population data for modeling the movement of locust swarms
Include a cover letter indicating that the submission is intended for this special section of JARS. Authors are required to follow the guidelines found on the journal’s website for preparing their manuscript(s), and, if possible are encouraged to pay the voluntary publication charges. Submissions based on reports that have been published in a conference proceedings should adhere to the guidelines on the journal's website. Please visit the JARS website at http://spie.org/jars for further information.
High-Performance Computing in Applied Remote Sensing
Guest Editors:
Bormin Huang
University of Wisconsin-Madison
Space Science and Engineering Center
1225 West Dayton Street
Madison, Wisconsin 53706
Tel: +1-608-265-2231
E-mail: bormin@ssec.wisc.edu
Antonio Plaza
Hyperspectral Computing Laboratory
University of Extremadura
Avda. de la Universidad s/n
E-10003 Caceres, Spain
Tel: +34-927-257000 (Ext. 51662)
E-mail: aplaza@unex.es
Call for Papers: Technological advances in modern active and passive sensors with higher spectral, spatial, and/or temporal resolutions have resulted in a substantial increase in multidimensional data volume. The increase poses a challenge to processing remote sensing data in a more timely fashion for environmental, commercial, or military applications. Parallel, distributed, and grid computing facilities and algorithms have become indispensable tools to tackle the issues of processing massive remote sensing data. In recent years, the graphics processing unit (GPU) has evolved into highly parallel many-core processor with tremendous computing power and high memory bandwidth to offer two to three orders of magnitude speedup over the central processing unit (CPU). A cost-effective GPU-based computer has become an appealing alternative to an expensive CPU-based computer cluster for many researchers performing various scientific and engineering applications. This special section of the Journal of Applied Remote Sensing (JARS) will present the state-of-the-art research in incorporating high-performance computing (HPC) facilities and algorithms for effective and efficient remote sensing applications. Papers are solicited in, but not limited to, the following areas:
• HPC applications in remote sensing image and video coding/decoding and error correction.
• HPC applications in spaceborne, airborne, or ground-based sensor design and simulation.
• HPC applications in geophysical parameter retrieval from remote sensing data.
• HPC applications in remote sensing data assimilation and modeling for environmental and weather monitoring and forecast.
• HPC applications in multispectral, hyperspectral, or ultraspectral remote sensing data processing.
• HPC applications in microwave, visible, or ultraviolet remote sensing data processing.
• HPC applications in radar and lidar remote sensing data processing.
• HPC applications in passive and active remote sensing data processing, including image registration, color correction, noise reduction, image tracking, target detection, spectral unmixing, feature extraction, image segmentation, image recognition, data fusion, super-resolution, anomaly detection, etc.
A cover letter indicating that the submission is intended for this special section of JARS should be included with the paper.
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