Remote Sensing and Earth Intelligence in Wildfire Ecology and Forest Restoration Monitoring

Dear Colleagues,

Under the context of global climate change, wildfires are increasing in frequency, intensity, and spatial extent, posing growing threats to ecosystem stability, carbon cycling, biodiversity conservation, and human societies. Traditional approaches to wildfire management and forest restoration assessment rely heavily on field surveys, which are time‑consuming, labor‑intensive, and often lack the capacity for large‑scale, spatially continuous, and timely dynamic monitoring. The 3S technologies—remote sensing (RS), geographic information systems (GIS), and global positioning systems (GPS)—are now being deeply integrated with artificial intelligence, cloud computing, digital twins, and other Earth Intelligence approaches, offering transformative pathways to address these challenges. From multi‑source satellite imagery, UAV‑based hyperspectral and LiDAR data to real‑time IoT sensing and intelligent analytical platforms, Earth Intelligence technologies enable fine‑scale characterization and dynamic modeling of pre‑fire risks, active fire behavior, and post‑fire ecological responses across multi‑dimensional spatiotemporal scales, driving a paradigm shift in wildfire ecology and forest restoration research from static observation toward intelligent decision‑making.

This Special Issue aims to bring together cutting‑edge research on wildfire ecology and forest restoration monitoring, with a focus on innovative applications of remote sensing and Earth Intelligence technologies. The goal is to establish an interdisciplinary platform that bridges remote sensing science, fire ecology, forest management, and artificial intelligence research, facilitating a deeper understanding of fire-driving mechanisms, post‑fire ecosystem recovery dynamics, and climate adaptation strategies. This topic aligns closely with the scope of Remote Sensing, which publishes high‑quality research on remote sensing theory, techniques, and applications in environmental, ecological, and earth sciences. By focusing on forest ecosystems, this Special Issue will highlight the potential of remote sensing and Earth Intelligence technologies in addressing global climate challenges and enhancing ecosystem resilience.

Submissions may include, but are not limited to, the following topics:

• Retrieval, prediction, and burned area extraction methods for fire extent, severity, and behavior based on multi‑source remote sensing and artificial intelligence.
• Fuel load assessment, pre‑fire risk modeling, and prediction integrating LiDAR, UAV, and cloud computing.
• Earth Intelligence‑driven dynamic early warning and near‑real‑time monitoring approaches for lightning‑induced and wildland fires.
• Long‑term monitoring of post‑fire forest recovery: from vegetation structure to ecosystem functions.
• Remote sensing analysis and earth system modeling of coupled fire‑vegetation‑climate interactions.
• Application of digital twins and intelligent decision support systems in forest restoration effectiveness assessment.
• Fire emissions, air quality, and human health impacts based on multi‑source remote sensing and atmospheric modeling.
• Machine learning and large‑scale remote sensing time-series analysis for post‑fire landscape pattern dynamics.
• Earth Intelligence‑based adaptive forest fire management strategies under climate change, integrating traditional wildfire management approaches.

Dr. Yakui Shao
Prof. Dr. Guang Yang
Prof. Dr. Qiuhua Wang
Prof. Dr. Jia Wang
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-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.

• wildfire ecology
• forest restoration
• earth intelligence
• remote sensing
• artificial intelligence
• unmanned aerial vehicle (UAV)
• cloud computing
• digital twin
• fire severity
• burned area
• burned area extraction
• fire behavior
• fuel load
• lightning-induced fire
• dynamic early warning
• near real-time monitoring
• fire vegetation climate coupling
• landscape pattern