Xiaodong Liu

Myanmar has developed a range of legality assurance and traceability mechanisms to support legal timber production and respond to evolving international market requirements. This study examines the institutional development of timber legality assurance and traceability in Myanmar’s forest supply chains and its implications for verification, market access, and trade. A qualitative research design was employed, combining thematic analysis of 200 publicly available documents collected between January and May 2026 with descriptive analysis of timber production and trade statistics from successive editions of the International Tropical Timber Organization (ITTO) Biennial Review and Assessment of the World Timber Situation. The trade analysis used annual production, import, export, and domestic consumption data for 2010–2024 together with direction-of-trade statistics for 2013–2023. The findings show that Myanmar has established formal institutional arrangements for timber legality assurance through the Myanmar Timber Legality Assurance System (MTLAS), the Myanmar Timber Chain of Custody (CoC) Process, the Myanmar Forest Certification Scheme (MFCS), and the Digital Timber Traceability System (DTTS). Documentary evidence indicates progressive institutional development while providing comparatively limited evidence regarding operational effectiveness. Descriptive trade analysis identified structural changes in timber exports and increasing orientation towards regional Asian markets. The review demonstrates that the principal challenge lies not only in establishing legality assurance mechanisms but also in maintaining transparent, reliable, and independently verifiable information capable of supporting evolving international due-diligence requirements.

Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual synthesis of foundational fire-science literature. The evidence base contains 179 unique references, including an AI-focused corpus, classical deterministic and probabilistic fire-danger and spread models, global ignition and lightning studies, remote-sensing and fuel-moisture foundations, decision-support tools, and governance literature. We define prevention-facing AI as systems that support pre-ignition or pre-escalation decisions and compare studies by data source, model design, validation protocol, forecast horizon, transferability, interpretability, and management action. The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability. AI is therefore most useful when it is hybrid, interpretable, and deployment-aware: it should complement established fire-weather and spread-model baselines while converting ecological observations into timely and actionable prevention judgments.

Global public safety is increasingly threatened by frequent road traffic accidents, making early accident risk prediction essential for intelligent transportation systems. However, incomplete multimodal data and insufficient spatiotemporal modeling lead to unstable performance in real-world scenarios with sensor failure, data loss, or visual occlusion. To address these issues, we propose the Early Accident Risk Multi-Level Spatiotemporal Robustness Network (EAR-MLSR-Net). Unlike conventional GCN-LSTM or Transformer-based architectures that separately model spatial and temporal dependencies, EAR-MLSR-Net introduces a multi-level spatiotemporal learning paradigm, which jointly models local spatial interactions, regional propagation patterns, and global temporal evolution in a unified hierarchical framework, enabling more structured dependency learning across scales. The proposed EAR-MLSR-Net follows a hierarchical multi-level spatiotemporal learning paradigm that integrates spatial reasoning, cross-modal temporal alignment, and multi-scale temporal dynamics modeling into a unified framework. Experiments conducted on the large-scale CAP-DATA dataset, which contains over 11,727 traffic scenarios and multimodal sensor records, demonstrate that EAR-MLSR-Net achieves an AUC of 0.860, an AP of 0.764, and a TTA_0.5 of 4.252 s. Compared with the strongest baseline, the proposed method achieves a 1.40% improvement in AUC and a 2.10% improvement in AP, while reducing performance degradation by 0.012 under missing-modality conditions.

Lightning-ignited wildfires are an increasing hazard in boreal forests, with their frequency amplified by global warming and more frequent thunderstorms. However, the mechanisms governing lightning-induced ignition and the subsequent smoldering–flaming transition remain poorly understood. This study aims to understand the ignition mechanisms of lightning-induced forest fires by combining a physics-based heat-balance model and controlled laboratory simulations. Experiments were conducted using twelve representative surface fuel types collected from six typical forest types in the Daxing’anling region, a lightning fire-prone area in northern China. Three fundamental stages of fire behavior development were systematically investigated, including the lightning-induced ignition, smoldering propagation, and the smoldering-to-flaming transition. Fuel moisture content was varied from 5% to 45%, and wind speed was adjusted between 0 and 5 m/s. The results demonstrated that discharge energy and wind speed significantly increased ignition probability, while fuel moisture content was negatively correlated with smoldering spread rate. Wind speed showed the greatest influence on the smoldering-to-flaming transition. The findings provide new mechanistic insights into the thermal and physical processes driving lightning-induced fires, supporting predictive modeling of ignition thresholds and fire behavior under changing meteorological and fuel conditions.

Forest certification in developing countries faces significant challenges due to weak institutions, limited market incentives, and complex trade conditions. This study investigates the status and key constraints of the Myanmar forest certification sector through a survey of 180 stakeholders from government organizations, NGOs, INGOs, third-party certification bodies, and private plantation owners, complemented by quantitative analysis and qualitative interviews. The results indicate a moderate level of familiarity with the Myanmar forest certification standard and high awareness of the Myanmar Forest Certification Committee; however, progress remains slow due to limited transparency, poor institutional coordination, financial and technical constraints, and insufficient stakeholder involvement. Non-compliances issues identified during pilot audits were primarily related to incomplete documentation, unclear land tenure, and weaknesses in environmental assessment. Geopolitical factors continue to limit Myanmar’s participation in certified timber markets and weaken efforts to improve traceability. Experiences from Indonesia, Malaysia, and Vietnam highlight that developing credible national certification systems requires time, clear legal frameworks, and strong cooperation among stakeholders. Strengthening institutional capacity, improving transparency, and aligning national standards with international forest governance frameworks are essential for Myanmar to build trust, achieve sustainable forest management, and regain market access.

Increasing wildfires are causing global concerns about ecosystem functioning and services. Although some wildfires are caused by natural ignitions, it is also important to understand how human ignitions and human-related factors can contribute to wildfires. While dynamic global vegetation models (DGVMs) have incorporated fire-related modules to simulate wildfires and their impacts, few models have fully considered various human-related factors causing human ignitions. Using global examples, this study aims to identify key factors associated with human impacts on wildfires and provides suggestions for enhancing model simulations. The main categories explored in this paper are human behavior and activities, socioeconomic background, policy, laws, regulations, and cultural and traditional activities, all of which can influence wildfires. Employing an integrated and interdisciplinary assessment approach, this study evaluates existing DGVMs and provides suggestions for their improvement.

This study assesses habitat suitability and identifies conservation priority areas for the endangered Marco Polo sheep throughout Khunjerab National Park (Pakistan) and Tashkurgan Natural Reserve (China). We analyzed species occurrence records against environmental variables (elevation, slope, climate, land cover) using MaxEnt modeling. Model performance was validated through AUC-ROC analysis and response curves, generating spatial predictions of suitable habitats to inform conservation strategies. Spatial predictions were generated to map potential distribution zones, aiding conservation planning for this endangered species. The model’s predictive performance was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic curve, yielding an AUC of 0.919, indicating strong discriminatory capability. Elevation (43.9%), slope (25.9%), and September precipitation (15.9%) emerged as the most influential environmental predictors, collectively contributing 85.7% to the model. The total percentage contribution and permutation significance values were 98.6% and 77.8%, respectively. Jackknife analysis identified elevation (bio-1), slope (bio-7), hillshade (bio-2), and the maximum July temperature (bio-9) as the most significant factors influencing the distribution of Marco Polo sheep, Conversely, variables such as viewshade (bio-14), land cover (bio-3), and precipitation in August (bio-4) contributed a minimal gain, suggesting that they had little impact on accurately predicting species distribution. The habitat suitability map reveals varying conditions across the study area, with the highest suitability (yellow zones) found in the northern and western regions, particularly along the Wakhan Corridor ridgelines. The southern regions, including Khunjerab Pass, show predominantly low suitability, marked by purple zones, suggesting poor habitat conditions. The eastern region displays moderate to low suitability, with fragmented patches of green and yellow, indicating seasonal habitats. The survival of transboundary Marco Polo sheep remains at risk due to poaching activities and habitat destruction and border fence barriers. This study recommends scientific approaches to habitat restoration together with improved China–Pakistan cooperation in order to establish sustainable migratory patterns for this iconic species.

In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, forest fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing ecological and economic losses, improving forest fire management efficiency, and ensuring personnel safety and property security. To enhance comprehensive understanding of wildfire prediction research, this paper systematically reviews studies since 2015, focusing on two key aspects: datasets with related tools and prediction algorithms. We categorized the literature into three categories: statistical analysis and physical models, traditional machine learning methods, and deep learning approaches. Additionally, this review summarizes the data types and open-source datasets used in the selected literature. The paper further outlines current challenges and future directions, including exploring wildfire risk data management and multimodal deep learning, investigating self-supervised learning models, improving model interpretability and developing explainable models, integrating physics-informed models with machine learning, and constructing digital twin technology for real-time wildfire simulation and fire scenario analysis. This study aims to provide valuable support for forest natural resource management and enhanced environmental protection through the application of remote sensing technologies and artificial intelligence algorithms.