INTERNATIONAL JOURNAL OF REMOTE SENSING

Scope & Guideline

Unveiling the Secrets of Our Planet from Above

Introduction

Delve into the academic richness of INTERNATIONAL JOURNAL OF 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.
LanguageMulti-Language
ISSN0143-1161
PublisherTAYLOR & FRANCIS LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1980 to 2024
AbbreviationINT J REMOTE SENS / Int. J. Remote Sens.
Frequency24 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND

Aims and Scopes

The International Journal of Remote Sensing focuses on the application of remote sensing technologies to various fields, emphasizing the integration of innovative methodologies and interdisciplinary approaches.
  1. Remote Sensing Technology Development:
    The journal publishes research on the advancement of remote sensing technologies, including satellite, aerial, and ground-based systems, with a focus on sensor innovation, data acquisition, and processing techniques.
  2. Environmental Monitoring and Management:
    Research addressing environmental challenges such as deforestation, land degradation, climate change, and urbanization, utilizing remote sensing data for monitoring and management purposes.
  3. Agricultural and Land Use Studies:
    Papers frequently explore applications in agriculture, including crop monitoring, precision agriculture, land use classification, and food security assessments using remote sensing data.
  4. Climate and Atmospheric Studies:
    The journal covers studies related to climate monitoring, atmospheric phenomena, and their interactions with land and water resources, leveraging remote sensing for data collection and analysis.
  5. Disaster Management and Risk Assessment:
    Research focusing on the use of remote sensing for disaster risk assessment, response, and recovery, including studies on floods, wildfires, and other natural disasters.
  6. Data Fusion and Machine Learning Applications:
    The integration of remote sensing data with machine learning techniques for enhanced analysis, classification, and prediction across various applications.
Recent trends in the International Journal of Remote Sensing highlight a shift towards innovative methodologies, interdisciplinary applications, and advanced analytical techniques.
  1. Machine Learning and AI in Remote Sensing:
    There is a significant increase in research applying machine learning and artificial intelligence techniques to remote sensing data analysis, enhancing classification, prediction, and interpretation capabilities.
  2. Multi-Source Data Integration:
    Emerging themes focus on the integration of data from multiple sources, including satellite, UAV, and ground-based observations, to provide more comprehensive analyses and insights.
  3. High-Resolution Remote Sensing Applications:
    An upward trend in studies utilizing high-resolution remote sensing data for applications in urban planning, environmental monitoring, and precision agriculture is evident, reflecting the growing availability of such data.
  4. Time-Series Analysis and Monitoring:
    Papers focusing on time-series analysis for monitoring environmental changes, land use dynamics, and vegetation health are increasingly common, emphasizing the need for continuous monitoring.
  5. Remote Sensing for Biodiversity and Ecosystem Studies:
    Research exploring the use of remote sensing in assessing biodiversity, ecosystem health, and conservation efforts is gaining momentum, driven by the need for effective environmental management strategies.

Declining or Waning

While the journal has maintained a strong focus on emerging technologies and applications, certain traditional themes in remote sensing research appear to be declining in prominence.
  1. Traditional Land Cover Mapping Techniques:
    There is a noticeable decline in the publication of papers focusing on conventional land cover mapping methods, as newer approaches utilizing advanced machine learning and remote sensing technologies gain traction.
  2. Single-Sensor Remote Sensing Studies:
    Research limited to single-sensor data analysis has decreased, with a shift towards multi-sensor approaches that integrate various data sources for comprehensive insights.
  3. Static Environmental Assessments:
    Papers that provide static assessments of environmental conditions are becoming less common, as there is a growing preference for dynamic, time-series analyses that capture changes over time.
  4. Basic Remote Sensing Algorithms:
    The publication of studies focusing solely on basic remote sensing algorithms without innovative applications or enhancements has declined, reflecting a shift towards more complex and application-driven research.

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