Multi-Source Remote Sensing Approaches for Monitoring and Conserving Forest Biodiversity: Advances in Methodology and Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 453
Editors
Interests: forest ecology; phenology; diversity; pest monitoring; forest management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forests harbour the majority of Earth’s terrestrial biodiversity, yet monitoring their composition, structure and dynamics at relevant scales remains a critical challenge. Recent advancements in multi-source remote sensing, combining satellite, airborne, drone and in situ data, offer unprecedented opportunities to observe forest biodiversity with higher accuracy, spatio-temporal resolution and accessibility. This Special Issue invites submissions that showcase innovative methodologies, interdisciplinary applications and scalable solutions for forest biodiversity observation.
Aim
This Special Issue aims to showcase cutting-edge multi-source remote-sensing techniques and their application in advancing forest biodiversity observation, with particular focus on the following:
- Data fusion and integration across different sensors (LiDAR, hyperspectral, SAR, drones, satellite constellations, etc.).
- Scalable solutions from local to global scales (e.g., combining field data with airborne/satellite observations).
- Novel algorithms/methodologies to mitigate gaps in biodiversity monitoring (e.g., species-specific indicators, understory monitoring, carbon–biodiversity interactions).
- Interdisciplinary approaches linking remote sensing with ecology, conservation and policy (e.g., protected area management, REDD+ or global biodiversity frameworks like the KTB).
Scope
We invite manuscripts addressing, but not limited to, the following:
- Methodological Innovations: Fusion of active/passive sensing (e.g., LiDAR + hyperspectral) for biodiversity metrics; machine-learning/deep-learning applications for species classification or habitat mapping and validation of remote sensing methods using in situ biodiversity data (e.g., plot networks, citizen science).
- Biodiversity Metrics and Indicators: Tracking above/below-ground biodiversity (e.g., canopy structure, understory diversity); monitoring keystone/endangered species or functional traits and linking biodiversity to ecosystem services (e.g., pollination, biomass, carbon storage).
- Scalability and Accessibility: Open-data solutions for remote-sensing biodiversity applications; case studies from tropical, temperate or boreal forests and integration with global initiatives (e.g., GBIF, TRY, EarthCube).
Exclusions:
- Purely theoretical or non-remote-sensing studies (unless directly connected to validation/integration).
- Manuscripts lacking original data, methodological novelty or clear applications to forest biodiversity.
Potential topics:
- Mapping endemism or endemic-rich areas
- Detecting forest disturbances (logging, fire, pests) via biodiversity signatures
- Low-cost drones/sensors for biodiversity monitoring in data-sparse regions
- AI/ML breakthroughs for species-specific remote sensing
- Integration with global databases (e.g., GBIF, GEO BON).
Dr. Marco Bascietto
Dr. Adriano Palma
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 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-anonymized 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
- Primary keywords:
- multi-source remote sensing
- multi-sensor fusion
- forest biodiversity
- hyperspectral
- LiDAR
- SAR integration
- sentinel missions
- drone-based ecology
- AI for biodiversity
- forest canopies
- understory monitoring
- ecosystem services
- conservation remote sensing
- species detection
- validation protocols
- data fusion algorithms
- Secondary Keywords:
- canopy diversity
- structural complexity
- functional trait mapping
- rare species detection
- global biodiversity monitoring
- GBIF integration
- REDD+
- citizen science for remote sensing
- cost-effective sensing
- forest resiliency indicators
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