Revista de Teledeteccion

Scope & Guideline

Connecting researchers to real-world solutions.

Introduction

Delve into the academic richness of Revista de Teledeteccion 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
ISSN1133-0953
PublisherUNIV POLITECNICA VALENCIA, EDITORIAL UPV
Support Open AccessYes
CountrySpain
TypeJournal
Convergefrom 2012 to 2024
AbbreviationREV TELEDETEC / Rev. Teledetec.
Frequency2 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressCAMINO VERA S-N, VALENCIA 46022, SPAIN

Aims and Scopes

The journal 'Revista de Teledeteccion' is dedicated to the advancement of remote sensing technologies and methodologies for environmental monitoring and management. Its core research areas encompass a wide range of applications in natural resources, urban studies, and climate change, utilizing innovative techniques and data sources.
  1. Remote Sensing Applications:
    Focus on the application of remote sensing technologies for monitoring various environmental parameters such as land use, vegetation cover, and water resources.
  2. Data Integration and Analysis:
    Emphasizes the integration of remote sensing data with ground-based measurements and other datasets to enhance the accuracy and reliability of environmental assessments.
  3. Machine Learning and AI Techniques:
    Utilizes advanced machine learning algorithms for classification, prediction, and analysis of environmental data, showcasing the journal's commitment to innovative methodologies.
  4. Environmental Impact Studies:
    Investigates the effects of human activities and climatic changes on natural ecosystems, addressing critical issues like urban expansion, deforestation, and water management.
  5. Geospatial Modeling and Assessment:
    Involves the development and application of geospatial models to understand complex environmental processes and inform policy decisions.
Recent publications in 'Revista de Teledeteccion' reveal several trending and emerging themes that highlight the journal's responsiveness to contemporary environmental challenges and technological advancements. These themes reflect a shift towards more innovative and integrative approaches in remote sensing research.
  1. Machine Learning for Environmental Monitoring:
    There is a growing emphasis on the use of machine learning techniques for environmental monitoring, as seen in studies applying classification algorithms to various datasets, indicating a trend towards more sophisticated analytical methods.
  2. Integration of Google Earth Engine:
    The adoption of Google Earth Engine for data processing and analysis is increasingly prevalent, showcasing a trend towards utilizing cloud-based platforms for large-scale environmental assessments.
  3. Climate Change Impact Assessments:
    Research focusing on the impacts of climate change, particularly on water resources and vegetation dynamics, has gained prominence, reflecting the urgency of addressing environmental sustainability.
  4. High-Resolution Remote Sensing:
    The trend towards utilizing high-resolution satellite imagery and UAV data for detailed environmental analyses is evident, indicating a shift towards precision in remote sensing applications.
  5. Spatiotemporal Analysis of Environmental Changes:
    Emerging studies are increasingly adopting spatiotemporal approaches to analyze environmental changes over time, indicating a shift towards understanding the dynamics of ecosystems rather than static assessments.

Declining or Waning

While 'Revista de Teledeteccion' has consistently focused on various aspects of remote sensing, certain themes appear to be losing traction in recent publications. This decline may reflect shifts in research priorities or advancements in technology that render previous methodologies less relevant.
  1. Traditional Remote Sensing Techniques:
    There has been a noticeable decrease in the publication of studies focused solely on traditional remote sensing techniques, as researchers increasingly adopt more sophisticated approaches such as machine learning and AI.
  2. Local Case Studies:
    Research focusing exclusively on specific local case studies without broader implications or applications has become less common, possibly due to a growing emphasis on regional or global analyses.
  3. Static Environmental Assessments:
    The journal has seen a decline in papers that provide static snapshots of environmental conditions, reflecting a shift towards dynamic and temporal analyses that consider changes over time.
  4. Basic Land Cover Classification:
    Basic methodologies for land cover classification without the integration of advanced algorithms or remote sensing innovations have diminished, as researchers pursue more complex and data-driven approaches.

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