Artificial Intelligence for Forest Remote Sensing: Methods, Applications and Emerging Trends

Dear Colleagues,

Forest ecosystems play a vital role in maintaining global biodiversity, regulating the climate, conserving water resources, and supporting sustainable socio-economic development. Accurate and timely information on forest resources is therefore essential for sustainable forest management, biodiversity conservation, carbon accounting, ecosystem restoration, and disaster prevention. With the rapid development of Earth observation technologies, including satellite imagery, unmanned aerial vehicles (UAVs), LiDAR, hyperspectral imaging, and synthetic aperture radar (SAR), forest remote sensing has entered the era of multi-source and multi-scale data acquisition.

Meanwhile, artificial intelligence (AI) has fundamentally transformed the way remote sensing data are processed and interpreted. From traditional machine learning to deep learning, transformer architectures, multimodal learning, foundation models, and intelligent agents, AI technologies have significantly improved the accuracy, efficiency, and automation of forest information extraction and decision support. AI-driven approaches are now widely applied to forest inventory, tree species classification, biomass estimation, carbon monitoring, forest health assessment, wildfire detection, biodiversity monitoring, and precision forest management. Nevertheless, challenges remain in terms of data heterogeneity, model generalization, interpretability, multi-source data fusion, and the deployment of AI models in real-world forestry applications.

This Special Issue aims to provide an international platform for presenting the latest advances in artificial intelligence for forest remote sensing, covering methodological innovations, practical applications, and emerging research trends. It seeks to bring together researchers from the fields of remote sensing, computer vision, geospatial artificial intelligence (GeoAI), forestry, ecology, and environmental sciences to share cutting-edge research and interdisciplinary solutions.

This Special Issue welcomes contributions that develop novel AI algorithms, improve remote sensing data interpretation, integrate multi-source observations, and support intelligent forest monitoring and management. It also encourages studies that demonstrate innovative applications of AI in forest ecosystems and explore future directions such as foundation models, multimodal learning, explainable AI, digital twins, and intelligent decision support.

The topics of this Special Issue are closely aligned with the scope of Remote Sensing, particularly in advancing remote sensing methodologies, intelligent information extraction, Earth observation technologies, environmental monitoring, and geospatial data analysis for forest ecosystems.

(1) Artificial intelligence and machine learning for forest remote sensing;

(2) Deep learning and computer vision for forest information extraction;

(3) Foundation models, large language models, and vision-language models for forest applications;

(4) Multi-source remote sensing data fusion (satellite, UAV, LiDAR, SAR, hyperspectral, multispectral);

(5) Wildfire monitoring, risk assessment, and post-fire recovery;

(6) Intelligent agents and AI-assisted decision support in forestry.

Dr. Xueyan Zhu
Prof. Dr. Huaguo Huang
Dr. Xihan Mu
Dr. Xun Zhao
Guest Editors

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• forest remote sensing
• multi-source remote sensing
• geospatial artificial intelligence (GeoAI)
• digital twins
• forest monitoring
• artificial intelligence