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Advances in Forest Degradation and Deforestation Monitoring with AI and Multi-Source Remote Sensing Data

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

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 1478

Special Issue Editors

Prof. Dr. Ran Meng

E-Mail Website
Guest Editor
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150008, China
2. National Key Laboratory of Smart Farming Technologies and Systems, Harbin 150008, China
Interests: multi-source remote sensing; vegetation dynamics; plant stress
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huaguo Huang

E-Mail Website
Guest Editor
Forestry College, Beijing Forestry University, Beijing 100083, China
Interests: 3D vegetation radiative transfer model; remote sensing methods for forestry ground surveys; application of radiative transfer models in forestry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huabing Huang

E-Mail Website
Guest Editor
School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou 510275, China
Interests: vegetation height and biomass mapping; dynamics land cover monitoring

Special Issue Information

Dear Colleagues,

Forest degradation and deforestation caused by different drivers (e.g., fire, logging, urbanization, plant diseases/pests, and agriculture expansion) are critical environmental issues that have profound implications for biodiversity, climate change, and ecosystem services across the globe. The integration of Artificial Intelligence (AI) and multi-source remote sensing data has emerged as a transformative approach to monitor and understand these processes with unprecedented accuracy and efficiency. Remote sensing technologies, including optical, radar, and LiDAR systems, provide vast amounts of spatial and temporal data, while AI techniques, such as traditional machine learning, deep learning, and recent remote sensing multimodal large models, enable the extraction of meaningful insights from these complex datasets. This convergence of technologies offers a powerful toolkit for detecting, quantifying, and predicting these forest changes, thereby supporting informed decision-making and sustainable forest management. Given the urgency of addressing global degradation, deforestation, and their associated environmental impacts, this research area has gained significant scientific and societal importance, making it a focal point for interdisciplinary collaboration and innovation. 

This Special Issue, ‘Advances in Forest Degradation and Deforestation Monitoring with AI and Multi-Source Remote Sensing Data’, aims to showcase cutting-edge research that leverages AI and remote sensing to advance our understanding of forest degradation and deforestation. This Special Issue seeks to highlight novel methodologies, tools, and applications that address the challenges of monitoring forest degradation and deforestation across diverse ecosystems and scales. By bringing together contributions from experts in remote sensing, AI, ecology, and environmental science, this Special Issue will provide a comprehensive platform for disseminating innovative solutions and fostering interdisciplinary dialogue. The topic aligns closely with the scope of Remote Sensing, which emphasizes the development and application of remote sensing technologies to address pressing environmental issues. This Special Issue will not only advance the scientific community’s knowledge, but also contribute to global efforts in forest conservation and climate change mitigation. 

We invite submissions that explore a wide range of themes, including, but not limited to, the following: the development of AI-driven algorithms for remote sensing-based forest change detection; the integration of multi-source remote sensing data for enhanced monitoring accuracy of forest disturbances; the application of deep learning techniques in forest degradation and deforestation monitoring; and the use of remote sensing for assessing the impacts of deforestation on biodiversity and carbon stocks. Both original research articles and review papers are encouraged, as well as case studies that demonstrate the practical application of these technologies in real-world scenarios. Additionally, contributions that address the challenges of data availability, scalability, and interpretability in AI-based remote sensing approaches are particularly welcome. By encompassing these diverse themes, the Special Issue aims to provide a holistic perspective on the current state and future directions of forest degradation and deforestation monitoring, ultimately driving progress in this critical field.

Prof. Dr. Ran Meng
Prof. Dr. Huaguo Huang
Prof. Dr. Huabing Huang
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 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. 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

  • vegetation dynamics
  • vegetation structure parameters
  • multi-source remote sensing
  • artificial intelligence
  • radiative transfer model
  • smart forestry

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Published Papers (2 papers)

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Research

22 pages, 57371 KB  
Article
Individual Planted Tree Seedling Detection from UAV Multimodal Data with the Alternate Scanning Fusion Method
by Taoming Qi, Yaokai Liu, Junxiang Tan, Pengyu Yin, Changping Huang, Zengguang Zhou and Ziyang Li
Remote Sens. 2025, 17(21), 3650; https://doi.org/10.3390/rs17213650 - 5 Nov 2025
Abstract
Detection of planted tree seedlings at the individual level is crucial for monitoring forest ecosystems and supporting silvicultural management. The combination of deep learning (DL) object detection algorithms and remote sensing (RS) data from unmanned aerial vehicles (UAVs) offers efficient and cost-effective solutions. [...] Read more.
Detection of planted tree seedlings at the individual level is crucial for monitoring forest ecosystems and supporting silvicultural management. The combination of deep learning (DL) object detection algorithms and remote sensing (RS) data from unmanned aerial vehicles (UAVs) offers efficient and cost-effective solutions. However, current methods predominantly rely on unimodal RS data sources, overlooking the multi-source nature of RS data, which may result in an insufficient representation of target features. Moreover, there is a lack of multimodal frameworks tailored explicitly for detecting planted tree seedlings. Consequently, we propose a multimodal object detection framework for this task by integrating texture information from digital orthophoto maps (DOMs) and geometric information from digital surface models (DSMs). We introduce alternate scanning fusion (ASF), a novel multimodal fusion module based on state space models (SSMs). The ASF can achieve global feature fusion while maintaining linear computational complexity. We embed ASF modules into a dual-backbone YOLOv5 object detection framework, enabling feature-level fusion between DOM and DSM for end-to-end detection. To train and evaluate the proposed framework, we establish the planted tree seedling (PTS) dataset. On the PTS dataset, our method achieves an AP50 of 72.6% for detecting planted tree seedlings, significantly outperforming the original YOLOv5 on unimodal data: 63.5% on DOM and 55.9% on DSM. Within the YOLOv5 framework, comparative experiments on both our PTS dataset and the public VEDAI benchmark demonstrate that the ASF surpasses representative fusion methods in multimodal detection accuracy while maintaining relatively low computational cost. Full article
(This article belongs to the Special Issue Advances in Forest Degradation and Deforestation Monitoring with AI and Multi-Source Remote Sensing Data)
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27 pages, 6300 KB  
Article
From Trends to Drivers: Vegetation Degradation and Land-Use Change in Babil and Al-Qadisiyah, Iraq (2000–2023)
by Nawar Al-Tameemi, Zhang Xuexia, Fahad Shahzad, Kaleem Mehmood, Xiao Linying and Jinxing Zhou
Remote Sens. 2025, 17(19), 3343; https://doi.org/10.3390/rs17193343 - 1 Oct 2025
Viewed by 839
Abstract
Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on [...] Read more.
Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on vegetation degradation risk than anthropogenic pressures, conditional on hydrological connectivity and irrigation. Using Babil and Al-Qadisiyah (2000–2023) as a case, we implement a four-part pipeline: (i) Fractional Vegetation Cover with Mann–Kendall/Sen’s slope to quantify greening/browning trends; (ii) LandTrendr to extract disturbance timing and magnitude; (iii) annual LULC maps from a Random Forest classifier to resolve transitions; and (iv) an XGBoost classifier to map degradation risk and attribute climate vs. anthropogenic influence via drop-group permutation (ΔAUC), grouped SHAP shares, and leave-group-out ablation, all under spatial block cross-validation. Driver attribution shows mid-term and short-term drought (SPEI-06, SPEI-03) as the strongest predictors, and conditional permutation yields a larger average AUC loss for the climate block than for the anthropogenic block, while grouped SHAP shares are comparable between the two, and ablation suggests a neutral to weak anthropogenic edge. The XGBoost model attains AUC = 0.884 (test) and maps 9.7% of the area as high risk (>0.70), concentrated away from perennial water bodies. Over 2000–2023, LULC change indicates CA +515 km2, HO +129 km2, UL +70 km2, BL −697 km2, WB −16.7 km2. Trend analysis shows recovery across 51.5% of the landscape (+29.6% dec−1 median) and severe decline over 2.5% (−22.0% dec−1). The integrated design couples trend mapping with driver attribution, clarifying how compounded climatic stress and intensive land use shape contemporary desertification risk and providing spatial priorities for restoration and adaptive water management. Full article
(This article belongs to the Special Issue Advances in Forest Degradation and Deforestation Monitoring with AI and Multi-Source Remote Sensing Data)
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