Special Issue "Remote Sensing for Biodiversity Mapping and Monitoring"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Dr. David Sheeren
E-Mail Website
Guest Editor
DYNAFOR Lab., University of Toulouse, INRA, F-31326 Castanet Tolosan, France
Interests: remote sensing of biodiversity; machine learning for earth observation; time series; hyperspectral imagery; forest ecosystems; landscape ecology
Special Issues and Collections in MDPI journals
Dr. Jean-Baptiste Féret
Co-Guest Editor
TETIS Lab., IRSTEA, F-3400 Montpellier, France
Interests: remote sensing of vegetation; biodiversity mapping; vegetation biophysical properties; imaging spectroscopy; tropical ecosystems; physical modeling; leaf traits
Special Issues and Collections in MDPI journals
Dr. Laurence Hubert-Moy
E-Mail Website
Co-Guest Editor
LETG-Rennes, University of Rennes 2, F-35043 Rennes, France
Interests: remote sensing of agricultural landscapes; land use and cover changes; remote sensing of grasslands and wetlands; habitat mapping
Special Issues and Collections in MDPI journals
Dr. Sophie Fabre
Co-Guest Editor
ONERA (The French Aerospace Lab.), DOTA, F-31000 Toulouse, France
Interests: hyperspectral imagery; multitemporal change detection; species mapping; vegetation health; anthropogenic impact assessment

Special Issue Information

Dear Colleagues,

Biodiversity is facing dramatic erosion and increasing pressure caused by anthropogenic activities intensifying in magnitude and extent. This leads to strong transformations impacting ecosystems globally, including changes in taxonomic diversity, as well as structure and functions at plant and ecosystem scales. These transformations need to be observed, assessed, and reported with dedicated monitoring programs in order to build efficient actions to mitigate or reverse them. Such monitoring programs need to rely on remote sensing (RS) data, as they potentially provide spatially explicit information from the Earth’s surface at regional to global scale, with regular revisit time, and collect information particularly relevant for the monitoring of vegetated surfaces. Such information is crucial to understanding how biodiversity responds to global environmental changes and directs human activity.

This Special Issue aims to publish original research that specifically addresses various aspects of biodiversity mapping and monitoring over space and time using remote sensing from local to global scales. We invite a wide range of contributions from methodological to applied and multidisciplinary research about the following (non-exclusive) topics:

  • Taxonomic, structural, and functional diversity mapping from RS data;
  • Species distribution modeling based on RS data;
  • Retrieving biophysical and biochemical variables from RS data and radiative transfer models;
  • Assessing and predicting ecosystem services from RS data;
  • Ecosystems health monitoring from RS data;
  • Reconstructing ecosystem trajectories over time from RS data;
  • Advanced machine learning techniques (deep learning, transfer learning, active learning) for biodiversity mapping based on RS data;
  • Fusion of multimodal images (optical/thermal/radar/lidar) to improve biodiversity mapping and monitoring.

Reviews covering one or more topics are welcome.
We encourage the authors to make their sample data and computational tools publicly available through online resources to ensure the reproducibility and transparency of all the experiments.

Dr. David Sheeren
Dr. Jean-Baptiste Féret
Dr. Laurence Hubert-Moy
Dr. Sophie Fabre
Guest Editors

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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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 2000 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.


  • Essential biodiversity variables
  • Biodiversity mapping
  • Species traits
  • Species diversity
  • Ecosystem functioning
  • Conservation
  • Earth observation
  • Diversity indices from space
  • Spectroscopy
  • Spatiotemporal change

Published Papers (1 paper)

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Open AccessArticle
Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data
Remote Sens. 2020, 12(1), 80; https://doi.org/10.3390/rs12010080 - 24 Dec 2019
Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing [...] Read more.
Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)
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