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AI-Driven Forestry Remote Sensing: Datasets, Models, Analysis and Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 20 February 2027 | Viewed by 3114

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Editors

Dr. Fang Huang

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Guest Editor

School of Recourses and Environment, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Interests: parallel computing; GIS; remote sensing; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Lingling Ma

E-Mail Website
Guest Editor

National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: calibration and validation techniques of remote sensing data and products; infight performance assessment of optical sensors; grassland forests and grasses

Prof. Dr. Jia Wang

E-Mail Website
Guest Editor

College of Forestry, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China
Interests: response of forest vegetation to climate change and human activities
Special Issues, Collections and Topics in MDPI journals

Dr. Peng Liu

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
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-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

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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Published Papers (4 papers)

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Research

26 pages, 16497 KB

Open AccessArticle

Analysis of the Variation Trends and Driving Forces of Growing-Season kNDVI in Hainan Island over the Past Three Decades

by Guangyang Li, Zongzhu Chen, Tingtian Wu, Xiaohua Chen, Xiaoyan Pan, Yuanling Li and Yiqing Chen

Remote Sens. 2026, 18(16), 2730; https://doi.org/10.3390/rs18162730 - 13 Aug 2026

Viewed by 170

Abstract

The construction of the Hainan Free Trade Port (FTP) is guided by the core philosophy of “ecological priority and green development.” To meet the practical requirements of building a “Green and Beautiful FTP,” this study constructed a kernel Normalized Difference Vegetation Index (kNDVI) dataset using Landsat series satellite imagery. By integrating methods including the Mann–Kendall (MK) trend test, Hurst exponent, and coefficient of variation (CV), an in-depth analysis was conducted on the spatiotemporal evolution characteristics and trends of growing-season vegetation in Hainan Island from 1994 to 2023. Additionally, the Extreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) interpretation method were employed to quantitatively unravel the driving mechanisms of climatic factors and human activities on kNDVI variations. The results indicate that: (1) Over the past three decades, the overall kNDVI of Hainan Island has exhibited a significant upward trend, characterized spatially by an evolution pattern of “stable recovery in the central region and localized degradation along the coast.” (2) The vegetation evolution demonstrates strong persistence and is highly consistent with community stability. The high stability in the central mountainous areas stems from a superior natural background and strict ecological protection; the transition zone is jointly influenced by vegetation types and anthropogenic management; meanwhile the coastal areas exhibit significant degradation characteristics driven by high-intensity human disturbances. (3) The analysis of driving mechanisms reveals a complex control logic of “topographical foundation—human reshaping—extreme climate triggering.” Static topographical factors, such as elevation and slope, occupy an absolute dominant position; human activities, represented by rubber plantation expansion and urbanization, exert a bidirectional reshaping effect characterized by “inland greening and coastal suppression”; furthermore, extreme drought and high temperatures in the later stages of the study demonstrated a significant pulse-like impact, exacerbating the risks of short-term climatic stress. This study clarifies the core patterns of vegetation evolution in Hainan Island, validates the effectiveness of ecological policies, and provides a quantitative scientific basis for ecological conservation and sustainable development in tropical island regions. Full article

(This article belongs to the Special Issue AI-Driven Forestry Remote Sensing: Datasets, Models, Analysis and Applications)

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31 pages, 3694 KB

Open AccessArticle

Transformer-Based Individual Tree Crown Detection from Canopy Height Models with Cross-Domain and Self-Supervised Pretraining

by Josué Gourde, Baoxin Hu and Qian Li

Remote Sens. 2026, 18(11), 1674; https://doi.org/10.3390/rs18111674 - 22 May 2026

Viewed by 722

Abstract

Individual tree crown (ITC) detection from remotely sensed data is fundamental to forest inventory and ecological monitoring, but deep learning approaches remain constrained by limited labelled training data. We systematically evaluate three transformer detectors (the Detection Transformer (DETR), Deformable DETR, and DETR with Improved DeNoising Anchor Boxes (DINO)) paired with two backbones, ImageNet-pretrained ResNet-50 and a Masked Autoencoder (MAE) pretrained on unlabelled Canopy Height Model (CHM) data. These are benchmarked against a classical local maximum and watershed pipeline and Faster R-CNN across four test sets spanning boreal, temperate mixed-wood, and diverse North American forest types at 0.25–1.0 m resolution. Spatially held-out test regions with a one-patch buffer band eliminate sliding-window leakage; headline configurations are reported as mean ± standard deviation across three random seeds. With multi-resolution MAE pretraining, the practical lower bound for non-degenerate single-dataset transformer detection lies between ∼200 and ∼1200 patches. Without MAE pretraining, DETR fails at every dataset size we tested. Multi-dataset joint training reaches

F_{1} = 0.84 \pm 0.01

on the boreal test set and 0.45–0.68 across the temperate-mixed-wood and NEON test sets, while Faster R-CNN narrowly wins on the smallest training pool. Standard DETR with ResNet-50 collapses regardless of the length of training schedule, but the same architecture with an MAE backbone reaches

F_{1} = 0.83 \pm 0.01

at that schedule, showing that DETR’s reported instability is conditional on the combination of backbone initialization and training budget rather than architectural. Resolution and backbone interact: ResNet-50 wins at 0.25 m, and MAE wins at 0.5–1.0 m, consistent with the eight-pixel MAE patch-matching crown scale only at coarser resolutions. Full article

(This article belongs to the Special Issue AI-Driven Forestry Remote Sensing: Datasets, Models, Analysis and Applications)

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32 pages, 3275 KB

Open AccessArticle

Machine Learning-Based Mapping of Dominant Tree Species in Dryland Forests Using Multi-Temporal and Multi-Source Data

by Emad H. E. Yasin, Milan Koreň and Kornel Czimber

Remote Sens. 2026, 18(8), 1185; https://doi.org/10.3390/rs18081185 - 15 Apr 2026

Cited by 1 | Viewed by 600

Abstract

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

(This article belongs to the Special Issue AI-Driven Forestry Remote Sensing: Datasets, Models, Analysis and Applications)

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31 pages, 6307 KB

Open AccessArticle

A Novel Urban Biological Parameter Estimation Method Based on LiDAR Point Cloud Single-Tree Segmentation

by Tongtong Lu, Fang Huang, Yuxin Ding, Qingzhe Lv, Hao Guan, Gongwei Li, Xiang Kang and Geer Teng

Remote Sens. 2026, 18(7), 1001; https://doi.org/10.3390/rs18071001 - 27 Mar 2026

Cited by 2 | Viewed by 732

Abstract

Aiming at diverse urban tree structures and difficulties in vegetation point cloud extraction and utilization, this study proposed single-tree-scale biological parameter estimation methods for urban scenarios to enhance point cloud’s application value in urban greening management. For single-tree segmentation, it constructed a method based on the constraints of the trees’ geometric features and combined the gravitational modeling characteristics, called the CGF-CG single-tree segmentation method. This method (i) combines clustering and principal direction analysis to extract trunk points, (ii) introduces canopy segmentation based on trunk positions, (iii) optimizes edge point attributes via a gravitational model. Based on CGF-CG’s accurate results, an improved random forest method for single-tree biological parameter (IRF-BP) estimation (aboveground biomass, carbon storage, leaf area index, living vegetation volume) was proposed: (i) correlation analysis with variable screening, (ii) adaptive feature selection and pigeon-inspired optimization to enhance model generalization, (iii) adopting Shapley Additive Explanations (SHAP) to improve interpretability. Based on these, a complete model for different tree species was constructed. Validation showed that CGF-CG exhibited negligible over-segmentation and under-segmentation in the selected study areas, with overall average precision, recall, and F1-score over 98.5%. Additionally, on the selected overall region, the overall mF1 score, mPTP, and mPTR of our method are 99.13%, 99.15%, and 99.12%, respectively, which are superior to Forestmetrics, lidR, PyCrown, and DBSCAN methods. IRF-BP performed well, with a highest R² of 0.81 and a lowest mean absolute percentage error of 7.5%, effectively surpassing the performance of traditional models such as RFR, GBR, KNN, and XGB. In summary, results provided theoretical and technical support for urban green resource management and evaluation. Full article

(This article belongs to the Special Issue AI-Driven Forestry Remote Sensing: Datasets, Models, Analysis and Applications)

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