AI-Enabled Precise Forest Monitoring Through UAV and Satellite Remote Sensing

Topic Information

Dear Colleagues,

Forest resources are a crucial ecological security barrier for the nation, and a precise and efficient monitoring system is the core support for the protection, management, and sustainable utilization of forest resources. Traditional forest monitoring methods suffer from drawbacks such as being time-consuming and labor-intensive, having limited coverage, untimely updates, and insufficient accuracy, making them ill-suited to the demands of high-quality forestry development and ecological protection in the new era. Unmanned Aerial Vehicle (UAV) remote sensing offers advantages such as flexibility, efficiency, high resolution, low cost, and the ability to perform precise operations, while satellite remote sensing provides wide-area coverage, long-term continuous monitoring, and macroscopic monitoring capabilities. Integrating these two methods with technologies such as artificial intelligence and big data can construct a comprehensive, intelligent, and routine forest resource monitoring system.

This Topic encourages the submission of empirical research and theoretical papers in the fields of environmental science, geography, and remote sensing science that aim to provide new technologies and methods to support the sustainable utilization of forest ecosystem services. Papers employing interdisciplinary approaches are particularly welcome, including, but not limited to, the following:

• Research on intelligent monitoring of forest resources based on UAVs and satellite remote sensing; UAV/airborne/spaceborne technologies for forest surveying;
• Forest resource surveys and management using UAVs equipped with sensors (RGB cameras, LiDAR, GNSS, IMU, hyperspectral cameras, etc.);
• Research on remote sensing applications in forest canopy height and attribute measurement, biomass estimation, pest and disease mapping, forest and biodiversity mapping, canopy gap mapping, and forest fire monitoring;
• Advances in ecosystem modeling for estimating forest variables and solving forest mapping problems;
• Recent advances in optical remote sensing technologies for assessing carbon storage and sequestration in forest ecosystems and biodiversity trends.
• Building forest stock volume inversion models based on high-resolution imagery/LiDAR point clouds acquired by satellite imagery and UAVs

Prof. Dr. Huaqiang Du
Prof. Dr. Xiaoli Zhang
Prof. Dr. Dengsheng Lu
Prof. Dr. Ying Yu
Dr. Xuejian Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Drones drones	5.2	10.0	2017	21.1 Days	CHF 2600	Submit
Forests forests	3.1	5.4	2010	17.3 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit

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Published Papers (4 papers)

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All Drones Forests Remote Sensing

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22 pages, 10031 KB  

Open AccessArticle

Remote Sensing Estimation and Spatiotemporal Variation Characteristics of Forest Aboveground Carbon Storage in Qianjiangyuan Baishanzu National Park

by Lei Huang, Xuejian Li, Fangjie Mao, Zihao Huang and Huaqiang Du

Remote Sens. 2026, 18(11), 1791; https://doi.org/10.3390/rs18111791 - 1 Jun 2026

Viewed by 276

Abstract

National forest parks play an important role in maintaining the integrity of ecosystems, the sustainability of biodiversity, and the improvement of ecological service functions. Aboveground carbon storage (AGC) is an important indicator for evaluating forest ecosystem functions. Qianjiangyuan–Baishanzu National Park, located in the [...] Read more.

National forest parks play an important role in maintaining the integrity of ecosystems, the sustainability of biodiversity, and the improvement of ecological service functions. Aboveground carbon storage (AGC) is an important indicator for evaluating forest ecosystem functions. Qianjiangyuan–Baishanzu National Park, located in the southern part of Lishui City, serves as a typical representative of the mid-subtropical evergreen broad-leaved forest ecosystem. It is characterized by high biodiversity and serves as a concentration area for rare and endangered species. Therefore, assessing the spatiotemporal dynamics of forest AGC in the typical subtropical forests of Qianjiangyuan–Baishanzu National Park is of great scientific significance. Taking Qianjiangyuan–Baishanzu National Park as a case study, this research utilized three periods of Landsat satellite remote sensing data (2009, 2014, and 2019) alongside contemporaneous ground-based AGC survey samples. Feature variables were extracted and subsequently screened using the Boruta algorithm. There were three algorithms, including least squares (LS), support vector regression (SVR), and random forest (RF), constructed to estimate forest AGC. The optimal AGC estimation model was selected, and the spatiotemporal variation characteristics of forest AGC within the national park were systematically analyzed. Research has shown that (1) texture features are important parameters for constructing forest AGC estimation models, accounting for up to 71%, with the 7 × 7 window having the greatest impact. (2) All three models can achieve high accuracy in estimating the forest AGC and its spatial distribution in Qianjiangyuan Baishanzu National Park. Among them, the RF model has the highest overall accuracy in estimating forest AGC, with a training set R² of 0.938, RMSE of 5.550 Mg/ha, rRMSE of 12.517%, a test set R² of 0.954, RMSE of 4.653 Mg/ha, and rRMSE of 10.087%. (3) The distribution of forest AGC in Qianjiangyuan Baishanzu National Park shows significant spatial heterogeneity, with higher carbon storage in the central, southern, and southeastern regions, while the northern region has relatively lower carbon storage. From 2009 to 2019, the forest AGC in the Qianjiangyuan–Baishanzu National Park exhibited a steady annual increase, with AGC density rising from 37.64 Mg/ha to 66 Mg/ha and total AGC stock increasing from 2.16 Tg C to 4.36 Tg C. These findings provide precise data support and a scientific basis for decision-making regarding differentiated ecological carbon enhancement and functional zone management within the national park. Full article

(This article belongs to the Topic AI-Enabled Precise Forest Monitoring Through UAV and Satellite Remote Sensing)

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29 pages, 30646 KB  

Open AccessArticle

Precision Estimation of Aboveground Carbon Stock in Acidosasa edulis Bamboo Forests: A Fusion Approach with UAV-LiDAR, Allometric Equations, and Machine Learning

by Xiaoyu Guo, Weisen Wang, Zhanghua Xu, Mingjing Li, Kele Yang, Yan Tan, Ze Shi, Haohao Yue and Juncheng Zhang

Remote Sens. 2026, 18(9), 1431; https://doi.org/10.3390/rs18091431 - 4 May 2026

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Abstract

As a fast-growing and multifunctional crop, bamboo plays a pivotal role in food security and climate change mitigation by leveraging its high carbon sequestration potential. Monitoring aboveground carbon (AGC) stock in bamboo forests is crucial for guiding field management, growth observation, and yield [...] Read more.

As a fast-growing and multifunctional crop, bamboo plays a pivotal role in food security and climate change mitigation by leveraging its high carbon sequestration potential. Monitoring aboveground carbon (AGC) stock in bamboo forests is crucial for guiding field management, growth observation, and yield prediction. Unmanned aerial vehicle (UAV)-based point cloud sensors offer a rapid and scalable solution for measuring bamboo AGC. This study evaluates the potential of UAV-LiDAR and machine learning (ML) for organ-level AGC estimation in bamboo forests. From LiDAR point clouds, we extracted structural features—including height, density, canopy, and intensity metrics—aggregated by mean plot-level metric (Mean-PM) and maximum plot-level metric (Max-PM) values at a 1 m² grid scale. Key predictors were selected using ML-based recursive feature elimination (ML-RFE) to develop organ-specific AGC inversion models. Results showed that organ-specific carbon content and allometric equations effectively eliminated biases associated with a uniform coefficient. Max-PM features outperformed Mean-PM features in stem and leaf AGCs, with the XGBoost and Random Forest models achieving the highest accuracy (R² = 0.82 for stems, 0.73 for leaves). Height percentiles and canopy structural metrics emerged as dominant predictors. This UAV-LiDAR-ML framework provides a cost-effective solution for precise bamboo carbon estimation, offering critical insights for carbon neutrality management and informed decision-making in bamboo forest ecosystems. Full article

(This article belongs to the Topic AI-Enabled Precise Forest Monitoring Through UAV and Satellite Remote Sensing)

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26 pages, 12932 KB  

Open AccessArticle

Accurate Regional Above-Ground Biomass Mapping: Canopy Height-Constrained Upscaling from In Situ to Satellite Data

by Qiyu Guo, Jinbao Jiang, Xiaojun Qiao, Kangning Li, Xuzhe Yan and Yinpeng Zhao

Remote Sens. 2026, 18(8), 1170; https://doi.org/10.3390/rs18081170 - 14 Apr 2026

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Abstract

Accurate estimation of forest above-ground biomass (AGB) is essential for quantifying forest carbon stocks and supporting regional carbon accounting. However, regional AGB mapping requires the integration of field observations with satellite data, and the associated scale transformation often causes the loss of spatial [...] Read more.

Accurate estimation of forest above-ground biomass (AGB) is essential for quantifying forest carbon stocks and supporting regional carbon accounting. However, regional AGB mapping requires the integration of field observations with satellite data, and the associated scale transformation often causes the loss of spatial detail and reduced estimation consistency. To address this issue, this study proposes a forest canopy height-constrained area-to-area regression kriging (CCAM) method for upscaling UAV-derived AGB and generating a high-precision wall-to-wall AGB map for artificial forests in the sandy lands of northwest Liaoning Province, China. The framework integrates RFE-SVM-based feature selection, XGBoost-based UAV-AGB modeling, and CHM-constrained residual correction within a Regression-then-Kriging (R-K) strategy, while also evaluating the effects of moving-window size, scale transition, and the order of regression and kriging on upscaling performance. The results showed that the reconstructed UAV-AGB model achieved the highest accuracy, with R² = 0.91 and rRMSE = 0.12, providing a reliable 0.1 m AGB baseline for subsequent upscaling. Among the tested moving-window sizes, the $7\times 7$ window was identified as optimal. Under this setting, CCAM achieved R² = 0.81 and rRMSE = 0.08, substantially outperforming direct GF-2-based estimation (R² = 0.49, rRMSE = 0.24). The final 2 m regional AGB map further attained a validation accuracy of R² = 0.79 and rRMSE = 0.18. These results demonstrate that CCAM can effectively preserve fine-scale UAV-derived biomass information during scale transformation and provide a reliable pathway for linking UAV and satellite observations in regional forest AGB mapping. Full article

(This article belongs to the Topic AI-Enabled Precise Forest Monitoring Through UAV and Satellite Remote Sensing)

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17 pages, 17693 KB  

Open AccessArticle

High-Resolution Mapping of Eucalyptus Plantations for Municipal Forest Governance: A Task-Specific Deep Learning Approach in Nanning, China

by Boyuan Zhuang and Qingling Zhang

Forests 2026, 17(4), 461; https://doi.org/10.3390/f17040461 - 9 Apr 2026

Viewed by 443

Abstract

Eucalyptus plantations are expanding rapidly in southern China, delivering economic benefits but also posing ecological risks, which creates a pressing need for precise, municipal-scale monitoring. Mapping eucalyptus with sub-meter resolution imagery, however, is confronted by two main challenges: (1) the pronounced multi-scale heterogeneity [...] Read more.

Eucalyptus plantations are expanding rapidly in southern China, delivering economic benefits but also posing ecological risks, which creates a pressing need for precise, municipal-scale monitoring. Mapping eucalyptus with sub-meter resolution imagery, however, is confronted by two main challenges: (1) the pronounced multi-scale heterogeneity of fragmented stands, and (2) the difficulty in achieving precise boundary delineation due to shadowed and complex canopy edges. To address these, this study makes two primary contributions. First, we present the Eucalyptus Semantic Segmentation Dataset (ESSD)—a high-quality, pixel-level annotated dataset that includes geographic coordinates to support reproducible research. Second, we propose SDCNet, a task-specific deep learning network optimized for eucalyptus mapping. SDCNet incorporates a redesigned SD-ASPP module that leverages Deep Over-parameterized Convolution (DO-Conv) to capture multi-scale features, alongside a novel Coordinated Self-Attention Mechanism (CSAM) to enhance the accuracy of canopy boundary detection. Ablation studies confirm the effectiveness of each component. In benchmark tests against seven state-of-the-art semantic segmentation models, SDCNet achieves superior performance, obtaining a per-class Intersection over Union (IoU) of 88.83% and an F1-score of 93.81% for eucalyptus—an improvement of +2.24% in IoU and +1.71% in F1-score over the strongest baseline. Applied to Nanning City, SDCNet produces the first 0.3 m resolution eucalyptus distribution map for the region. This map reveals a critical finding: within the watershed of the Xiyunjiang Reservoir—Nanning’s primary drinking water source—eucalyptus plantations cover more than 50% of the forested area. This result provides the first quantitative, high-resolution evidence of potential hydrological risk at a municipal scale. Our work establishes an integrated framework that bridges advanced remote sensing with actionable forest governance, offering scientifically grounded support for ecological risk assessment and sustainable land-use policy. Full article

(This article belongs to the Topic AI-Enabled Precise Forest Monitoring Through UAV and Satellite Remote Sensing)

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