AI-Driven Forestry Remote Sensing: Datasets, Models, Analysis 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: 15 August 2026 | Viewed by 1289
Special Issue Editors
Interests: parallel computing; GIS; remote sensing; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: calibration and validation techniques of remote sensing data and products; infight performance assessment of optical sensors; grassland forests and grasses
Interests: response of forest vegetation to climate change and human activities
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent information processing of remote sensing images
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forestry is the core barrier of global ecological security, a key carrier of carbon neutrality goals, and a critical front for biodiversity conservation. Its research depth and breadth directly affect the sustainable development of human society. From exploring the evolution laws of natural forest ecosystems to optimizing urban forestry ecological service functions, scientific research on forestry systems has always revolved around the core logic of "Cognition–Regulation–Optimization–Prediction". The key to upgrading this entire chain lies in the innovation and breakthrough of technological means.
Traditional forestry research faces prominent problems such as limited data sources, fragmented processing flow, low model interpretability, and limited application scenarios, which make it difficult for research results to accurately match actual needs, let alone support dynamic simulation and long-term prediction of complex forestry systems. Especially in the context of intensified global environmental change and the increasing demand for refined forestry management, traditional models are no longer able to cope with the challenges of multi-scale, high-dimensional, and strongly coupled forestry system research. How to integrate the process of "Data integration–Intelligent algorithm adaptation–Dynamic model construction–Multi-scenario application–Trend prediction simulation–Intelligent expression visualization" has become a core proposition that urgently needs to be solved in current forestry research.
The rapid development of artificial intelligence (AI) technology provides the core driving force for this proposition, and it empowers the entire forestry research workflow. It can break down the barriers of multi-source remote sensing data fusion, achieve efficient processing and feature extraction of massive data. High precision and interpretable forestry system models can also be constructed through intelligent methods, revealing the complex mechanistic relationship between forest distribution and structure and global change, and breaking through traditional technological bottlenecks. It can better support the intelligent application of forest resource assessment, carbon cycle monitoring, disaster warning and other scenarios, and visually present research results through visualization technology, providing accurate support for forestry decision-making.
This comprehensive technological empowerment not only drives forestry research from fragmentation to systematization, from experience driven to data-driven intelligence, but also provides scientific support for forestry resource management, ecological protection, and sustainable development in the context of global climate change, highlighting the important theoretical value and practical significance of this research field.
The Special Issue focuses on the deep integration innovation of AI intelligent algorithms and forestry remote sensing, systematically exploring the cutting-edge methodology and practical path of empowering the entire chain of forestry research with intelligent technology. The core goal is to break through the technological bottleneck of traditional forestry research, promote the intelligent integration of multi-source remote sensing data, optimize the construction of forestry system algorithms, accurately implement diverse application scenarios, and visualize and transform research results into decisions. This will provide new technological support for the accurate inversion of forestry indicators, dynamic simulation and trend prediction of forestry systems, and research on the relationship between forest distribution and structure and global change, and help forestry research transform towards systematization, intelligence, and precision.
This Special Issue welcomes end-to-end intelligent solutions based on multi-source image (optical/hyperspectral/thermal infrared, etc.) or three dimensional (3D) point cloud (LiDAR/photogrammetry, etc.) data, covering, but not limited to, the following: construction and standardization of multimodal forestry datasets, innovation of AI intelligent algorithms for complex forestry scenes, high-precision intelligent inversion models of land parameters, simulation and long-term prediction of forest dynamic changes, response relationships of forest distribution and structure to global changes, and 3D visualization and intelligent expression of forest scenes based on digital twin technology.
The journal Remote Sensing focuses on the innovative development and practical application of remote sensing technology and is committed to publishing interdisciplinary and high-quality cutting-edge research results. The theme of this Special Issue closely aligns with the journal’s positioning. On the one hand, it focuses on the intelligent processing and application of multi-source remote sensing data, deepening the practical boundaries of remote sensing technology in the forestry field. On the other hand, the interdisciplinary integration of AI intelligent technology and forestry research has expanded the innovative application scenarios of remote sensing technology, highlighting the core value of remote sensing technology in empowering ecological protection, resource management, and sustainable development. The high degree of compatibility between the two will provide a high-quality communication platform for researchers in the global forestry remote sensing field, promoting the coordinated development of theoretical research and technological applications in related fields.
We welcome original research papers. The topics of interest include, but are not limited to, the following:
- AI-driven multi-source remote sensing data fusion and intelligent processing technology.
- Innovation of AI intelligent algorithms and model optimization construction in forestry research.
- AI-empowered forestry key indicator inversion and accurate evaluation methods.
- Intelligent technology for dynamic simulation and trend prediction of forestry systems.
- Intelligent solutions for forest resource management and ecological protection.
- Practice of AI remote sensing technology for urban forestry and living environment assessment.
- Intelligent expression and visualization technology for forestry remote sensing research results.
- Intelligent analysis of the dynamic evolution of forest distribution/structure and ecological response mechanisms under the background of global change.
Dr. Fang Huang
Dr. Lingling Ma
Prof. Dr. Jia Wang
Dr. Peng Liu
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.
Keywords
- artificial intelligence in forestry remote sensing
- multi-source data fusion
- forest parameter intelligent inversion and modeling
- forest dynamic simulation and prediction
- forest structure and global change
- 3D point cloud analysis and applications
- remote sensing image analysis and applications
- digital twin & visualization
- smart forestry
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