期刊荟萃|Journal of Applied Remote Sensing

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※ Description:

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

※ Scope:

The Journal of Applied Remote Sensing is an electronic 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. Topics covered by the journal include, but are not limited to,  the following areas:

Theoretical Innovation: Exploration of advances in physical, mathematical, and optical modeling for signature band analyses toward change detection studies with high-end image processing, pattern recognition, machine learning, evolutionary computing, soft computing, data fusion,  and data assimilation techniques, such as:

• Advanced remote sensing theory for improving the understanding of global-, regional-, and local-scale atmospheric, oceanic, and terrestrial systems

• System engineering for on-board and on-ground data receiving, transmission compression, processing, distribution, access, archiving, and integration

• Multisensor andcross-mission image and data fusion technologies for improving remote sensing monitoring and modeling capability to aid in decision making 

• Information analysis, data mining, machine learning, evolutionary computing, soft computing, and data management for remote sensing imageries

• Multitarget tracking using multispectral, hyperspectral, radar, and lidar remote sensing theory

• Integrated remote sensing, communication, networking, and control technologies

Interdisciplinary Applications: Bidirectional or multidirectional interfaces among different remote sensing communities for highly interdisciplinary studies, such as:

• Experimental concepts for the optimal utilization of various types of remote sensing data

• Remote sensing target selection, data acquisition, algorithm development calibration, validation, and product applications

• Remote sensingin support of seamless integration of the following subject areas:

• climatology, meteorology,  and numerical weather prediction

• ecological restoration, agricultural production, and natural resources management with coupled numerical modeling

• energy sources exploration and management

• land-cover and land-use change,  land management, mining,  and urban planning

• water resources management and hydrological science

• natural and human hazard monitoring and management

• environmental quality monitoring and environmental pollution control

Photo-Optical Instrumentation and Design: Space satellite and airborne technology development in concert with ground-level receiving stations through sensors and platforms, communication networks, and orbital control strategies, such as: 

• Planning, design, and calibration, intercalibration, and validation requirements for satellite instruments,  platforms,  and systems

• Pre- and postlaunch systems and instrumentation with interface design integration, testing,  and packaging

• Space, airborne, and ground-based receiving technology development and transfer

• New orbital measurement concepts in support of remote sensing

• Remote sensing sensor technology development, technology transfer, and new instrumentation concepts

• Spacecraft and aircraft instrument navigation, coregistration, and measurement stability.

※ About the Journal：

• E-ISSN:1931-3195

• Publisher: SPIE

• Frequency: Quarterly (4 issues/year)

• Year Established: 2007

• Format: Onlineonly

• Impact Factor*: 1.107

• 5-Year Impact Factor*: 1.235

• CiteScoreTM2016: 1.26

• h5-index: 20(#14 in Remote Sensing)

※ Top Downloads:

• Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

• Rapid broad area search and detection of Chinese surface-to-air missile sites using deep convolutional neural networks

• Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy within tegration of ALOS2 and Sentinel-2 data

• Comparison of mosaicking techniques for airborne images from consumer-grade cameras

• Extracting distribution and expansion of rubber plantations from Landsat imagery using the C5.0 decision tree method

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