Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing, 2nd Edition

Dear Colleagues,

The use of sensors, signal processing, and image analysis has revolutionized forest ecology and resource management over the past two decades, and early advancements in remote sensing, such as satellite imagery and LiDAR, have laid the foundation for non-destructive forest monitoring, enabling researchers to map forest cover, estimate biomass, and track deforestation. The first edition of this Special Issue, published in November 2023, highlighted breakthroughs in sensor technology development (e.g., UAV-based multispectral sensors) and machine learning applications for forest classification. Since then, rapid progress in AI-driven signal processing, high-resolution imaging sensors, and edge computing has unlocked new possibilities for the real-time, fine-scale monitoring of forest ecosystems. Today, these technologies are still critical for addressing challenges like climate change, biodiversity loss, and sustainable forest management, making this second edition a timely update on cutting-edge methodologies and applications.

This Special Issue aims to bridge the gap between sensor innovation, data science, and forest ecology, showcasing how advanced sensing and processing techniques can enhance our understanding of forest dynamics and optimize resource management. The scope includes:

• Sensor technologies—the development and deployment of ground-based, airborne, or spaceborne sensors (e.g., LiDAR, hyperspectral cameras, and IoT sensors) for forest parameter estimation (e.g., tree height, canopy cover, or species composition).
• Signal and image processing—novel algorithms for noise reduction, feature extraction, change detection, and data fusion in forest-related datasets.
• Ecological applications—monitoring forest health (e.g., pest/disease detection), carbon cycling, biodiversity, and disturbance responses (e.g., wildfires or logging) using sensor-derived data.
• Resource management—tools for sustainable timber harvesting, protected area monitoring, and policy support (e.g., real-time forest inventory systems).

The Special Issue will feature state-of-the-art research on:

• AI-Powered Sensing: Deep learning models for automated tree species classification from UAV imagery or satellite data.
• Multimodal Data Fusion: The integration of LiDAR, radar, and optical imagery to improve forest biomass estimation accuracy.
• Nanoscale and IoT Sensors: Miniaturized sensors for monitoring microclimatic variables (e.g., humidity or CO₂) or plant physiological signals (e.g., leaf water potential).
• Edge and Cloud Computing: The real-time processing of sensor data in remote forests using edge devices coupled with cloud-based big data analytics for long-term trend analysis.
• Novel Imaging Techniques: Hyperspectral and thermal imaging for detecting early signs of forest stress (e.g., drought or invasive species).

We invite submissions covering, but not limited to, the following areas:

• UAV/RPA-based forest monitoring systems;
• Machine learning for forest image analysis;
• Sensor networks for real-time fire/pest detection;
• Satellite-derived forest carbon stock assessments;
• AI-driven tools for automated forest inventory;
• Challenges in sensor data scalability and interoperability.

Prof. Dr. Yili Zheng
Dr. Paul Sestras
Prof. Dr. Yue Zhao
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 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. Forests is an international peer-reviewed open access monthly 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 2600 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.

• forest remote sensing
• sensor technology (e.g., LiDAR, hyperspectral imaging, UAV sensors)
• image and signal processing
• machine learning in forest ecology
• forest biomass estimation
• ecosystem health monitoring
• data fusion (multimodal sensors)
• AI-driven forest management
• climate change and forest dynamics
• IoT in forest monitoring
• edge computing for forest data
• tree species classification
• forest carbon stock assessment
• real-time disturbance detection (e.g., wildfires, pests)