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Forests
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Remote Sensing Approach for Early Detection of Forest Disturbance
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Remote Sensing Approach for Early Detection of Forest Disturbance

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2148

Special Issue Editors

Dr. Yaohui Liu

E-Mail Website
Guest Editor
1. School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: remote sensing; artificial intelligence; natural hazards; ecological environment
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Li

E-Mail Website
Guest Editor
Institute of Estuarine and Coastal Zone, College of Marine Geosciences, Ocean University of China, Qingdao 266005, China
Interests: radar remote sensing; machine learning and change detection; coastal wetlands mapping; GNSS; UAV LiDAR; SAR; multispectral and hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jin Wang

E-Mail Website
Guest Editor Assistant
Institute of Resources and Environment Innovation, Shandong Jianzhu University, Jinan 250101, China
Interests: environmental remote sensing; surface deformation; forest ecosystem research; ecological planning; watershed health indicators; water resources utilization
Dr. Pingjie Fu

E-Mail Website
Guest Editor Assistant
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Interests: hyperspectral; remote sensing; remote sensing of environment; soil; water; vegetation diversity; remote sensing retrieval

Special Issue Information

Dear Colleagues,

Forests are vital to the global ecological balance, yet they face numerous threats from natural and anthropogenic disturbances. The early detection of these disturbances is crucial for effective forest management, conservation, and restoration. Remote sensing technology offers a powerful tool for forest monitoring at various spatial and temporal scales and for the timely identification of changes in forest health, structure, and function.

This Special Issue invites original research articles, reviews, and case studies that explore innovative remote sensing approaches for the early detection of forest disturbances, as well as the ecological environment. Topics of interest include, but are not limited to, the use of satellite and airborne sensors, LiDAR, UAV-based imaging, and advanced image processing techniques such as machine learning and artificial intelligence. We are particularly interested in studies that addressing the challenges of detecting subtle or gradual changes in forests and ecological environment, distinguishing between different types of disturbances, and integrating multi-source data for comprehensive monitoring, forest species diversity, and ecological factors.

Contributions that demonstrate practical applications in forest management or that offer insights into the ecological impacts of forest disturbances are strongly encouraged. By bringing together cutting-edge research in this field, this Special Issue aims to advance our understanding of how remote sensing can be utilized for proactive forest disturbance detection and ultimately contribute to the sustainability and resilience of ecosystems worldwide.

Dr. Yaohui Liu
Dr. Peng Li
Guest Editors

Dr. Jin Wang
Dr. Pingjie Fu
Guest Editor Assistants

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Forests is an international peer-reviewed open access monthly 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 2600 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

  • remote sensing
  • forest disturbance
  • forest monitoring
  • ecological environment
  • machine learning
  • artificial intelligence
  • high resolution
  • LiDAR
  • UAV
  • recovery and management
  • deep learning
  • multispectral remote sensing
  • infrared remote sensing

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

Published Papers (4 papers)

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Research

18 pages, 4714 KiB  
Article
Integrating Hyperspectral Images and LiDAR Data Using Vision Transformers for Enhanced Vegetation Classification
by Xingquan Shu, Limin Ma and Fengqin Chang
Forests 2025, 16(4), 620; https://doi.org/10.3390/f16040620 - 2 Apr 2025
Viewed by 381
Abstract
This study proposes PlantViT, a Vision Transformer (ViT)-based framework for high-precision vegetation classification by integrating hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data. The dual-branch architecture optimizes feature fusion across spectral and spatial dimensions, where the LiDAR branch extracts elevation and [...] Read more.
This study proposes PlantViT, a Vision Transformer (ViT)-based framework for high-precision vegetation classification by integrating hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data. The dual-branch architecture optimizes feature fusion across spectral and spatial dimensions, where the LiDAR branch extracts elevation and structural features while minimizing information loss and the HSI branch applies involution-based feature extraction to enhance spectral discrimination. By leveraging involution-based feature extraction and a Lightweight ViT (LightViT), the proposed method demonstrates superior classification performance. Experimental results on the Houston 2013 and Trento datasets show that PlantViT achieves an overall accuracy of 99.0% and 97.4%, respectively, with strong agreement indicated by Kappa coefficients of 98.7% and 97.2%. These results highlight PlantViT’s robust capability in classifying heterogeneous vegetation, outperforming conventional CNN-based and other ViT-based models. This study advances Unmanned Aerial Vehicle (UAV)-based remote sensing (RS) for environmental monitoring by providing a scalable and efficient solution for wetland and forest ecosystem assessment. Full article
(This article belongs to the Special Issue Remote Sensing Approach for Early Detection of Forest Disturbance)
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24 pages, 44313 KiB  
Article
Spatiotemporal Trend and Influencing Factors of Surface Soil Moisture in Eurasian Drylands over the Past Four Decades
by Jinyue Liu, Jie Zhao, Junhao He, Jianjia Qu, Yushen Xing, Rui Du, Shichao Chen, Xianhui Tang, Liang Wang and Chao Yue
Forests 2025, 16(4), 589; https://doi.org/10.3390/f16040589 - 28 Mar 2025
Viewed by 258
Abstract
Eurasian drylands are vital for the global climate and ecological balance. Quantifying spatiotemporal variations in surface soil moisture (SSM) is essential for monitoring water, energy, and carbon cycles. The suitability of recent global-scale surface soil moisture datasets for Eurasian arid and semi-arid regions [...] Read more.
Eurasian drylands are vital for the global climate and ecological balance. Quantifying spatiotemporal variations in surface soil moisture (SSM) is essential for monitoring water, energy, and carbon cycles. The suitability of recent global-scale surface soil moisture datasets for Eurasian arid and semi-arid regions has not been comprehensively evaluated. This study investigates spatiotemporal trends of five SSM products—MERRA-2, ESACCI, GLEAM, GLDAS, and ERA5—from 1980 to 2023. The performance of these products was evaluated using in situ station data and the three-cornered hat (TCH) method, followed by partial correlation analysis to assess the influence of environmental factors, including mean annual temperature (MAT), mean annual precipitation (MAP), potential evapotranspiration (PET), vapor pressure deficit (VPD), and leaf area index (LAI), on SSM from 1981 to 2018. The results showed consistent SSM patterns: higher values in India, the North China Plain, and Russia, and lower values in the Arabian Peninsula, the Iranian Plateau, and Central Asia. Regionally, MAT, PET, VPD, and LAI increased significantly (0.04 °C yr−1, 1.66 mm yr−1, 0.004 kPa yr−1, and 0.003 m2 m−2 yr−1, respectively; p < 0.05), while MAP rose non-significantly (0.29 mm yr−1). ERA5 exhibited the strongest correlation with in situ station data (R2 = 0.42), followed by GLEAM (0.37), ESACCI (0.28), MERRA2 (0.19), and GLDAS (0.17). Additionally, ERA5 showed the highest correlation (correlation = 0.72), while GLEAM had the lowest bias (0.03 m3 m−3) and ESACCI exhibited the lowest ubRMSE (0.03 m3 m−3). The three-cornered hat method identified ERA5 and GLDAS as having the lowest uncertainties (<0.03 m3 m−3), with ESACCI exceeding 0.05 m3 m−3 in northern regions. Across land cover types, cropland had the lowest uncertainty among the five SSM products, while forest had the highest. Partial correlation and dominant factor analysis identified MAP as the primary driver of SSM. This study comprehensively evaluated SSM products, highlighting their strengths and limitations. It underscored MAP’s crucial role in SSM dynamics and provided insights for improving SSM datasets and water resource management in drylands, with broader implications for understanding the hydrological impacts of climate change. Full article
(This article belongs to the Special Issue Remote Sensing Approach for Early Detection of Forest Disturbance)
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24 pages, 31002 KiB  
Article
Reducing Forest Fragmentation in Yunnan Province Dominated by Afforestation Projects
by Yan Ma, Shaohua Zhang, Kun Yang, Yan Rao, Xiaofang Yang, Wenxia Zeng, Jing Liu and Changyou Bi
Forests 2025, 16(4), 571; https://doi.org/10.3390/f16040571 - 25 Mar 2025
Viewed by 222
Abstract
As a critical ecological security barrier, Yunnan Province has significantly reduced forest fragmentation through ecological restoration programs in recent years. However, the optimization process of the forest landscape and the most effective ecological restoration projects remain unclear. Our study combined land use data [...] Read more.
As a critical ecological security barrier, Yunnan Province has significantly reduced forest fragmentation through ecological restoration programs in recent years. However, the optimization process of the forest landscape and the most effective ecological restoration projects remain unclear. Our study combined land use data with 13 driving factors, including meteorological and socioeconomic data, to analyze the spatial distribution, temporal dynamics, and key ecological restoration programs of forest fragmentation using dynamic and static indexes, morphological spatial pattern analysis, boosted regression tree models, and partitioned statistical methods. We found that over the past 30 years, FF has significantly decreased. Fragmentation was higher before 2000 but has steadily declined, with eastern regions more fragmented than western areas. Forest landscapes have transitioned from degradation to recovery, with core forest areas expanding by 6997.72 km2. Afforestation was the main driver, adding 238,109.21 km2 of forest cover, while reforestation contributed 17,254.47 km2, improving patch size and connectivity. Regionally, the southwest has lower fragmentation due to ample rainfall and reforestation efforts, while central and northeastern areas face higher fragmentation from drought, human activities, and urban expansion. Our study offers a scientific basis for formulating effective ecological restoration policies. Full article
(This article belongs to the Special Issue Remote Sensing Approach for Early Detection of Forest Disturbance)
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15 pages, 7129 KiB  
Article
Exploration and Empirical Study on Spatial Distribution of SOC at the Core Area in Coastal Tamarix Forests’ Inland Side of Changyi National Marine Ecological Area
by Ruiting Liu, Ping Han, Jin Wang, Huiqian Zong, Xuewan Zhang, Qianxun Chen, Feiyong Chen, Yufeng Du, Zhao Li, Yaohui Liu, Pingjie Fu, Xiaoxiang Cheng and Jingtao Xu
Forests 2025, 16(1), 169; https://doi.org/10.3390/f16010169 - 17 Jan 2025
Viewed by 763
Abstract
The forest soil carbon pool plays a vital role in terrestrial ecosystems, being of great significance for maintaining global balance, regulating the global carbon cycle, and facilitating ecological restoration. Shandong Changyi Marine Ecological Special Protection Area is the only state-level marine special protection [...] Read more.
The forest soil carbon pool plays a vital role in terrestrial ecosystems, being of great significance for maintaining global balance, regulating the global carbon cycle, and facilitating ecological restoration. Shandong Changyi Marine Ecological Special Protection Area is the only state-level marine special protection area in China with tamarisk as the main object of protection, and it is the largest continuous and the best preserved natural tamarisk forest distribution area on the mainland coast of China. Compared to other forested areas, research on the spatial distribution of SOC at the core area in coastal Tamarix forests’ inland side appears to be relatively scarce. Based on this, this paper takes the core area of the Changyi National Marine Ecological Special Protection Zone, located on the southern coast of Laizhou Bay, as the research subject, based on the potassium dichromate oxidation-external heating, one-way ANOVA, and Bonferroni methods, analyzing the spatial distribution of the SOC content inland of coastal Tamarix forests. The research yielded the following conclusions: (1) The surface layer (0–10 cm) contributes significantly to the total SOC content within a 0–60 cm depth, accounting for at least 31% and shows notable surface accumulation. (2) The combined SOC content in the surface and subsurface layers (10–20 cm) accounts for at least 50% of the total SOC content within a 0–60 cm depth, indicating the dominance of these two soil layers in carbon storage. (3) The SOC content decreases with the soil depth at all six sampling points within the 0–60 cm range, with a marked drop from 0–10 cm to 10–20 cm. (4) One-way ANOVA and multiple comparisons reveal that the soil depth significantly affects the SOC distribution, particularly between the surface and 20–30 cm layers (p < 0.001), indicating high robustness and statistical significance. (5) Horizontally, the total SOC at 0 m is 45% lower than at 2 m in the 0–60 cm layer. The SOC in the 0–20 cm layer fluctuates significantly with distance from the shrub trunk, while the SOC in the 30–60 cm layers is low and stable, with minimal variations with depth. In addition, this study found that the SOC content in the core area of the protected area is lower than that in the common forest ecosystem. In the future, scientific ecological restoration projects and management protection methods should be used to improve soil’s carbon storage and carbon sink capacity. These findings not only validate the patterns of SOC’s spatial distribution in coastal Tamarix forest wetlands but also provide a scientific basis for carbon assessment and the formulation of ecological protection measures in coastal wetlands. Full article
(This article belongs to the Special Issue Remote Sensing Approach for Early Detection of Forest Disturbance)
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