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Global Biospheric Monitoring with Remote Sensing (2nd Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 970

Special Issue Editor

Special Issue Information

Dear Colleagues,

Global biospheric monitoring is a critical for understanding the complex interactions between living organisms and their environment. The biosphere, encompassing all ecosystems on Earth, plays a fundamental role in regulating the climate, cycling nutrients, and supporting biodiversity. As our impact on the environment intensifies, resulting in deforestation, urbanization, and climate change, it becomes increasingly vital to effectively monitor the biosphere. Remote sensing technologies enable scientists to observe and quantify changes in vegetation cover, biomass, and the health of the ecosystem across vast and diverse landscapes. These observations provide essential data that enable researchers and policymakers to assess ecological dynamics, implement conservation strategies, and mitigate environmental degradation.

The aim of this Special Issue is to explore advancements in global biospheric monitoring using remote sensing techniques, with a focus on vegetation and ecosystem analysis. We welcome submissions that showcase innovative methodologies, applications, and case studies related to the evaluation of the biosphere. We welcome research articles, reviews, and empirical studies that address the following topics:

  • Remote sensing techniques for monitoring vegetation health and biomass across different ecosystems;
  • The assessment of land cover changes and their impacts on biodiversity and ecosystem services using remote sensing data;
  • The mapping and evaluation of carbon sequestration in terrestrial ecosystems through remote sensing methodologies;
  • The monitoring of phenological changes in vegetation and their implications for ecosystem functioning using remote sensing;
  • The integration of remote sensing data with ground-based observations to enhance our understanding of plant diversity and ecological interactions.

Dr. Ram Avtar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • global biospheric monitoring
  • remote sensing
  • ecosystem dynamics
  • vegetation changes
  • biodiversity assessment
  • carbon sequestration

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Published Papers (1 paper)

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24 pages, 1777 KB  
Systematic Review
Monitoring Biodiversity and Ecosystem Services Using L-Band Synthetic Aperture Radar Satellite Data
by Brian Alan Johnson, Chisa Umemiya, Koji Miwa, Takeo Tadono, Ko Hamamoto, Yasuo Takahashi, Mariko Harada and Osamu Ochiai
Remote Sens. 2025, 17(20), 3489; https://doi.org/10.3390/rs17203489 - 20 Oct 2025
Viewed by 565
Abstract
Over the last decade, L-band synthetic aperture radar (SAR) satellite data has become more widely available globally, providing new opportunities for biodiversity and ecosystem services (BES) monitoring. To better understand these opportunities, we conducted a systematic scoping review of articles that utilized L-band [...] Read more.
Over the last decade, L-band synthetic aperture radar (SAR) satellite data has become more widely available globally, providing new opportunities for biodiversity and ecosystem services (BES) monitoring. To better understand these opportunities, we conducted a systematic scoping review of articles that utilized L-band synthetic aperture radar (SAR) satellite data for BES monitoring. We found that the data have mainly been analyzed using image classification and regression methods, with classification methods attempting to understand how the extent, spatial distribution, and/or changes in different types of land use/land cover affect BES, and regression methods attempting to generate spatially explicit maps of important BES-related indicators like species richness or vegetation above-ground biomass. Random forest classification and regression algorithms, in particular, were used frequently and found to be promising in many recent studies. Deep learning algorithms, while also promising, have seen relatively little usage thus far. PALSAR-1/-2 annual mosaic data was by far the most frequently used dataset. Although free, this data is limited by its low temporal resolution. To help overcome this and other limitations of the existing L-band SAR datasets, 64% of studies combined them with other types of remote sensing data (most commonly, optical multispectral data). Study sites were mainly subnational in scale and located in countries with high species richness. Future research opportunities include investigating the benefits of new free, high temporal resolution L-band SAR datasets (e.g., PALSAR-2 ScanSAR data) and the potential of combining L-band SAR with new sources of SAR data (e.g., P-band SAR data from the “Biomass” satellite) and further exploring the potential of deep learning techniques. Full article
(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing (2nd Edition))
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