Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 30 August 2026 | Viewed by 1449

Share This Special Issue

Special Issue Editors

Prof. Dr. Jincheng Liu

E-Mail Website
Guest Editor

College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
Interests: smart forestry; remote sensing; geographic information system; 3D point cloud; model; climate change and carbon sequestration assessment
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jia Wang

E-Mail Website
Guest Editor

The College of Forestry, Beijing Forestry University, Beijing, China
Interests: forest inventory; remote sensing; 3S; LiDAR; smart forestry; forestry carbon sink
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tao Liu

E-Mail Website
Guest Editor

Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA
Interests: deep learning; UAV; multiview imagery; LiDAR analytics; forest health and carbon modeling

Prof. Dr. Weiheng Xu

E-Mail Website
Guest Editor

College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650233, China
Interests: forestry equipment and informatization; intelligent processing and application of remote sensing big data; regional ecological remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Zhichao Wang

E-Mail Website
Guest Editor

Mapping and 3S Technology Center, Beijing Forestry University, Beijing, China
Interests: forest ecology and ecological monitoring; environmental sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The urgent need for sustainable forest management and effective climate change mitigation has propelled precision forestry and accurate carbon sink assessment to the forefront of research. Rapid advancements in remote sensing technologies—spanning high-resolution satellite imagery, unmanned aerial vehicles (UAVs), LiDAR, hyperspectral sensing, and synthetic aperture radar (SAR)—combined with artificial intelligence (AI) and big data analytics, are revolutionizing forest monitoring. These technologies enable a paradigm shift from traditional, labor-intensive field surveys to real-time, high-precision, and large-scale observations. Accurate quantification of forest structure, biomass, and carbon stocks is now feasible, supporting global efforts toward carbon neutrality and ecosystem conservation.

This Special Issue focuses on cutting-edge remote sensing technologies for precision forestry and carbon sink assessment. It highlights innovations in 3D structure modeling (LiDAR, SfM, and 3D point clouds), multi-source data fusion (optical/SAR/UAV), and dynamic monitoring (forest structure/function changes). Aligned with Remote Sensing’s mission to advance technological innovation and interdisciplinary applications, it provides a platform to share methods and case studies that bridge remote sensing with sustainable forest management and carbon neutrality.

We welcome original research, reviews, and technical communications on the following topics:

Multi-source remote sensing data fusion (e.g., optical, SAR, LiDAR, and hyperspectral) for forest structure and biomass estimation;
UAV and satellite-based high-spatiotemporal-resolution monitoring of forest dynamics;
AI and deep learning for forest classification, change detection, and carbon stock mapping;
3D forest structure modeling using LiDAR and photogrammetric point clouds;
Remote sensing of forest carbon cycles and carbon sink assessment;
Hyperspectral and thermal remote sensing for forest health and stress monitoring;
Integration of remote sensing with IoT and ground-based observations;
Cloud computing and open-source platforms for large-scale forest monitoring;
Applications in forest fire, pest, and disease detection;
Digital twin development and intelligent decision support systems in forestry.

Prof. Dr. Jincheng Liu
Prof. Dr. Jia Wang
Prof. Dr. Tao Liu
Prof. Dr. Weiheng Xu
Dr. Zhichao 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.

Keywords

forest structure and function
forest dynamics 3D point cloud
advanced algorithms
multi-source data fusion
UAV remote sensing
carbon sink assessment
precision forestry management

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

27 pages, 14900 KB

Open AccessArticle

TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement

by Belal Shaheen, Minh-Hieu Nguyen, Bach-Thuan Bui, Shubham, Tim Wu, Michael Fairley, Matthew Zane, Michael Wu and James Tompkin

Remote Sens. 2026, 18(6), 867; https://doi.org/10.3390/rs18060867 - 11 Mar 2026

Viewed by 253

Abstract

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM–MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS’s depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment)

► Show Figures

Figure 1

20 pages, 3607 KB

Open AccessArticle

Forest Aboveground Carbon Storage in the Three Parallel Rivers Region: A Remote Sensing and Machine Learning Perspective

by Qin Xiang, Rong Wei, Chaoguan Qin, Lianjin Fu, Zhengying Li, Hailin He and Qingtai Shu

Remote Sens. 2026, 18(5), 756; https://doi.org/10.3390/rs18050756 - 2 Mar 2026

Viewed by 258

Abstract

Accurate estimation of forest aboveground carbon (AGC) is crucial for understanding the carbon cycle and formulating climate policies, yet it remains challenging in complex mountainous regions. This study used machine learning framework to estimate the spatiotemporal dynamics of AGC in the Three Parallel Rivers region of China from 2003 to 2024. By integrating China’s National Forest Continuous Inventory (NFCI) data with multispectral satellite imagery, we employed a two-stage feature selection strategy to identify key predictor variables. Among three ensemble algorithms tested, the Random Forest model achieved the optimal performance (R² = 0.74). The results indicated a net increase of 67.05 Tg in total AGC over the two decades, with a spatial pattern characterized by higher densities in the west and north. Geographical Detector analysis revealed that the driving forces were synergistic, with the interaction between temperature and population density exhibiting the most prominent explanatory capacity. This study provides a high-resolution (30 m) benchmark for AGC in a global biodiversity hotspot and underscores the critical role of ecological protection policies in enhancing carbon sequestration, offering valuable insights for managing similar mountain ecosystems worldwide. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment)

► Show Figures

Figure 1

16 pages, 2311 KB

Open AccessArticle

The Novel Models for Identifying the Vertical Structure of Urban Vegetation from UAV LiDAR Data

by Hang Yang, Rongxin Deng, Xinmeng Jing, Zhen Dong, Xiaoyu Yang, Jingyi Li and Zhiwen Mei

Remote Sens. 2026, 18(5), 692; https://doi.org/10.3390/rs18050692 - 26 Feb 2026

Viewed by 313

Abstract

Accurate quantification of vegetation vertical structure is crucial for analyzing the ecological functions of urban green spaces. However, constrained by the complexity of vegetation structure and spatial heterogeneity, current approaches for extracting vegetation vertical structure by airborne LiDAR have limitations in terms of layer boundary identification stability, threshold dependency, and ecological plausibility. This study developed two integrated UAV LiDAR-based stratification frameworks for identifying urban riparian vegetation vertical structure by combining established statistical modeling and signal processing techniques: (1) a Gaussian Mixture Model with Bayesian Information Criterion (GMM-BIC)-based probabilistic stratification framework; (2) a Savitzky–Golay filtering and Pruned Exact Linear Time (SG-PELT)-based change-point detection framework. Furthermore, the ecological height constraint was incorporated into the model to achieve biological adjustments. Two models were applied in the study area and compared using reference data. The results showed that the GMM-BIC method achieved an overall classification accuracy of 91.06%, with a macro-averaged F1-score of 87.77%, while the SG-PELT method attained an overall accuracy of 84.57%, with a macro-averaged F1-score of 79.20%. These results demonstrate that both models can effectively identify the vertical structure of urban vegetation. In particular, the two models exhibited distinct characteristics across different scenarios. The GMM-BIC model showed superior stratification accuracy in regions where vegetation height distribution displayed pronounced multi-peak characteristics and distinct differences among height segments. In comparison, the SG-PELT model demonstrated greater sensitivity in areas with significant height variation and clearly defined abrupt transitions between layers. These models could provide new methodologies for monitoring vegetation vertical structure and offer data support for biodiversity monitoring and ecological function assessment within urban ecosystems. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment)

► Show Figures

Figure 1

25 pages, 3178 KB

Open AccessArticle

A Machine Learning Framework for Daily Mangrove Net Ecosystem Exchange Prediction from 2000 to 2025

by Linlin Ruan, Li Zhang, Min Yan, Bowei Chen, Bo Zhang, Yuqi Dong and Jian Zuo

Remote Sens. 2026, 18(4), 667; https://doi.org/10.3390/rs18040667 - 22 Feb 2026

Viewed by 464

Abstract

Mangrove ecosystems are important blue carbon systems and play a critical role in understanding carbon cycling and responses to climate change. However, accurate regional estimation of Net Ecosystem Exchange (NEE) remains challenging due to the environmental complexity and spatial heterogeneity. This study combined eddy covariance observations from four mangrove sites along China’s southeastern coast (natural and restored mangrove forests) with multi-source remote sensing and environmental reanalysis data to construct three variable schemes (site observations only, with added vegetation indices, and comprehensive multi-source variables). We compared three machine learning models for daily NEE prediction, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM). The results showed that: (1) Restored and natural mangroves exhibited similar temporal NEE dynamics and consistently functioned as carbon sinks, restored mangrove sites showed greater cross-site variability. Among the study sites, CN-LZR exhibited the strongest cumulative carbon uptake. (2) Scheme 3 combined with the XGBoost algorithm achieved the highest predictive accuracy, reaching an R² of 0.73 across sites. Differences among machine learning models were primarily associated with their ability to capture nonlinear interactions between atmospheric and hydrological variables, with tree-based models outperforming SVM. (3) SHAP analysis indicated that radiation-related variables were the dominant drivers of NEE, while hydrological influences were site-dependent; and (4) Regional upscaling indicated that all sites consistently functioned as long-term carbon sinks, with CN-LZR exhibiting slightly higher daily mean carbon uptake than the other sites. This study presented the first machine learning framework for estimating daily-scale NEE in mangroves, providing methodological and data support for regional carbon flux assessment and blue carbon management. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment)

► Show Figures

Journal Menu

Journal Browser

Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI