Journal of Applied Remote Sensing

Scope & Guideline

Bridging Disciplines for a Better Understanding of Our Planet

Introduction

Delve into the academic richness of Journal of Applied Remote Sensing with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN-
PublisherSPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationJ APPL REMOTE SENS / J. Appl. Remote Sens.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225

Aims and Scopes

The Journal of Applied Remote Sensing focuses on the application of remote sensing technologies and methodologies to address various environmental and societal challenges. It serves as a platform for researchers to disseminate innovative techniques, algorithms, and case studies that advance the field of remote sensing and its applications across multiple domains.
  1. Remote Sensing Technologies:
    The journal covers a wide range of remote sensing technologies, including optical, radar, and hyperspectral systems, emphasizing their application in environmental monitoring, agriculture, urban planning, and disaster management.
  2. Data Processing and Analysis Techniques:
    It emphasizes advanced data processing and analysis techniques, including machine learning, deep learning, and statistical methods, to enhance the interpretation of remote sensing data.
  3. Environmental Monitoring and Management:
    The journal focuses on applications related to environmental monitoring, such as land cover change detection, climate change effects, water quality assessment, and biodiversity conservation.
  4. Integration of Multisource Data:
    Research on integrating various data sources, including satellite, aerial, and ground-based measurements, is a core aspect, aimed at improving the accuracy and reliability of remote sensing applications.
  5. Innovative Applications:
    The journal highlights innovative applications of remote sensing in various fields, including agriculture, forestry, urban studies, and disaster response, showcasing real-world impacts and advancements.
The Journal of Applied Remote Sensing has identified several emerging themes that reflect the current trends and future directions of research within the field. These themes indicate a shift towards more integrated, advanced, and application-oriented studies.
  1. Machine Learning and Deep Learning Applications:
    There is a substantial increase in the use of machine learning and deep learning techniques for analyzing remote sensing data, reflecting a trend towards leveraging AI for improved accuracy and efficiency in various applications.
  2. Multi-Source Data Fusion:
    The integration of data from multiple sources, including satellite, aerial, and ground-based observations, is gaining traction, as researchers explore how to combine these datasets for comprehensive environmental assessments.
  3. Precision Agriculture:
    Research focused on precision agriculture is on the rise, with studies exploring how remote sensing can optimize crop management, enhance yield prediction, and monitor agricultural practices in real-time.
  4. Climate Change Monitoring:
    An increasing number of publications are dedicated to the use of remote sensing for monitoring climate change impacts, such as changes in land use, vegetation dynamics, and water resources.
  5. Urban Remote Sensing:
    The application of remote sensing in urban studies, including urban heat island effects, land use changes, and smart city development, is becoming a prominent theme, reflecting growing urbanization challenges.

Declining or Waning

As the field of remote sensing evolves, certain themes within the Journal of Applied Remote Sensing have begun to see a decline in focus. This shift may reflect changing research priorities or advancements in technology that render previous methodologies less relevant.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in the publication of papers focusing solely on traditional statistical analysis methods for remote sensing data, as more sophisticated machine learning and deep learning approaches gain prominence.
  2. Basic Remote Sensing Theory:
    Research that primarily emphasizes foundational theories of remote sensing without practical applications or advancements in technology is becoming less common, as the community shifts towards applied and innovative methodologies.
  3. Conventional Image Processing Techniques:
    There is a waning interest in conventional image processing techniques that do not incorporate contemporary advancements such as AI and machine learning, as researchers increasingly seek more robust and efficient methods.
  4. Single-Sensor Studies:
    The focus on studies utilizing single-sensor data is declining, with a growing trend towards multi-sensor and multi-platform approaches that provide more comprehensive insights.
  5. Generalized Land Cover Classification:
    Papers presenting generalized land cover classification without detailed context or innovative approaches are appearing less frequently, as the field moves towards more nuanced and context-specific applications.

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