2026-04-02
Tijs Kuzee, Tommaso Locatelli, Suzanne Robinson, Mike P. Perks, Paul J. Burgess, Abdou Khouakhi
Temperate forests which provide vital ecosystem functions through the provision of timber resources, carbon sequestration, and recreational value are increasingly affected by extreme weather events, with wind and precipitation extremes (drought and excessive rainfall) posing significant challenges to forest resilience. This review synthesizes current knowledge on the impacts of wind and precipitation extremes on temperate forests, focusing on compound disturbance interactions, vulnerability factors, and recovery processes through a systematic review of 248 sources. Research concentrated on single disturbances, with drought and wind most frequently studied. Moreover, there is a focus on short-term resistance and recovery, with limited evidence on reorientation (i.e., transition to a new ecosystem state). Furthermore, we assess recent advancements in disturbance modeling, remote sensing, and machine learning for detecting and forecasting damage from these events. The key observation is that remote sensing and disturbance models are rapidly growing areas of study, but they are skewed toward single disturbance types and are highly specific to particular ecosystems. Machine learning has reduced this specificity and allowed for more data integration in recent years, although small-scale disturbance detection in remote sensing remains challenging owing to data availability limitations. By integrating climate, ecological, and management perspectives, this review concludes that future research and practice must explicitly integrate compound events into multi-hazard models, supported by strengthened long-term (remote sensing) monitoring networks, and adopt adaptive silvicultural strategies. Improved monitoring and multi-hazard modeling will enhance early warning, attribution, and predictive capacity, thereby supporting risk-informed decision-making and the design of targeted adaptive management interventions. Such shifts are essential to sustain ecosystem services and enhance forest resilience under increasing climate extremes.
DOI: 10.3389/ffgc.2026.17465102026-03-27
Felipe de Miguel-Díez, Ferréol Berendt, Ina Ehrhardt, Sergej Chmara, Tobias Cremer
IntroductionVital to forest management, forest roads enable operations such as timber harvesting, fire protection, and recreational access. However, their functionality is threatened by insufficient maintenance driven by limited financial and human resources, and constraints related to the availability and quality of information, as well as suitable tools for systematic condition assessment. Traditional visual inspections are widely used in Germany but are subjective, inconsistent, and difficult to scale. This study examines stakeholder perspectives on implementing a digital forest road condition monitoring system to support predictive maintenance approaches.MethodsTo evaluate the feasibility and stakeholder acceptance, semi-structured qualitative interviews were conducted with 30 representatives from five stakeholder categories across Germany. Thematic content analysis was applied to assess current maintenance practices, data needs, technical and legal constraints, and the stakeholders’ willingness to contribute across stages of the system (data collection, processing, provision, and use).ResultsStakeholders broadly recognized the potential value of digital forest road condition data for planning maintenance, estimating costs, and improving navigation. However, adoption is constrained by limited digital competence and staffing, financial barriers, data protection and access-right concerns, and coordination challenges associated with fragmented forest ownership. Participants emphasized the need for reliable, standardized, and user-friendly solutions, and highlighted that long-term implementation depends on viable business model configurations (e.g., subscription or pay-per-use access models and remuneration schemes for data collection, such as flat rate) spanning multiple stakeholders.DiscussionBy aligning stakeholder interests and addressing legal, technical, and financial barriers, such configurations can enable the long-term operability of digital forest roads monitoring systems. These findings provide a qualitative foundation for developing scalable implementation concepts and business model configurations for digital forest road monitoring that are transferable beyond the German context.
DOI: 10.3389/ffgc.2026.16689962026-03-24
Jesús Torralba, F. Mark Danson, Luis A. Ruiz, Jaime Almonacid-Caballer, Pablo Crespo-Peremarch, Juan Pedro Carbonell-Rivera
The impact of climate change on vegetation dynamics and wildfire risk has been a subject of considerable research interest. Live fuel moisture content (LFMC) is a critical factor in assessing fire risk and influencing fire ignition and behaviour. Satellite remote sensing techniques provide information on LMFC dynamics, but spatial and temporal resolution hinder understanding in structurally complex forests with interconnected tree and shrub layers. Multi-wavelength terrestrial laser scanning (TLS) sensors can measure the structural and spectral properties of forests and have demonstrated their potential for monitoring LFMC. However, studies of LFMC in shrubs are scarce despite their key role in fire spread. In this study, we investigated the capacity of a dual-wavelength SALCA (Salford Advanced Laser Canopy Analyser) TLS (1063 and 1545 nm) and the single-wavelength Trimble X6 (1500 nm) to estimate LFMC in six Mediterranean forest plots (135 individual plants, 18 species). Analysis at different separate plots and individual-species levels identified key factors affecting LFMC prediction using TLS. At plot level, linking spectral indices and LFMC is challenging due to species diversity in crown structures, ages, sizes and leaf types. Our results suggest that detector heating by solar radiation could alter the sensor calibration and reduce model accuracy. Nevertheless, in some areas the multiple linear regression models achieved an Radj2 up to 0.82 and an RMSE of 7.66%. At the species level, models showed stronger relationships with LFMC (Radj2ranging from 0.43 to 0.88) and a relatively low RMSE (RMSE from 1.92 to 3.97%). Overall, univariate relationships between LFMC and individual-wavelength reflectance were not consistent across species or most plots. Considering these results, combining the capacity of dual TLS devices to estimate LFMC with the structural information that they provide, open a potential to improve field work sampling for wildfire risk assessment. Extending the research to cover a wider range of tree, shrub and herbaceous species in the future will advance our understanding of LFMC dynamics and contribute to more accurate fire behaviour modelling.
DOI: 10.3389/ffgc.2026.17702252026-03-23
Guanglong Bao, Xiaohan Sun, Chenchen Fan, Wenwen Li, Xiaoming Li, Fei Yan, Tianran Zhao
BackgroundBlue-Green Space (BGS) is critical for mitigating the Urban Heat Island (UHI) effect amid rapid global urbanization. However, the comparative cooling efficiency of BGS landscape configuration versus simple area expansion remains understudied, particularly regarding long-term spatiotemporal non-stationarity in large-scale economic zones.MethodsThis study investigates the spatiotemporal evolution of BGS and its regulatory effects on Land Surface Temperature (LST) in the Yangtze River Economic Belt (YEB) from 2000 to 2024. Using Landsat-derived metrics, we employed Global/Local Moran’s I to identify thermal clustering and the Spatiotemporal Geographically Weighted Regression (GTWR) model to quantify non-stationary relationships, explicitly comparing the marginal cooling contributions of landscape pattern indices against area proportion (PLAND).ResultsThe YEB exhibited a “downstream cooling, mid-upstream warming” trend with significant spatial clustering. The GTWR model (R2 = 0.840) revealed that optimizing landscape configuration yields superior cooling benefits to mere area expansion in specific spatiotemporal contexts. High patch connectivity (CONTAG) significantly mitigated heat in early urbanization stages, while complex patch shapes (FRAC_MN) and edge density (ED) were more effective in high-density urban cores.ConclusionThese findings challenge the “area-first” planning paradigm, supporting an “efficiency-first” strategy. To maximize urban climate resilience, we propose differentiated optimization pathways that prioritize contiguous preservation in mountainous regions and hydrological connectivity in water-rich networks.
DOI: 10.3389/ffgc.2026.17620542026-03-20
Nuri Bozali
IntroductionMediterranean forest ecosystems are highly susceptible to natural wildfires under climate change, driven by rising temperatures, reduced precipitation, and pro¬longed dry periods. This study aimed to develop a climate-based natural wildfire susceptibility model using the Random Forest (RF) machine learning algorithm for the Emet Forest Management Directorate in the Mediterranean climate zone of Türkiye. This study considers only natural wildfires and excludes human-induced fire events.MethodsAll fire occurrence data used in the modeling process consist solely of recorded natural wildfire ignitions. The model incorporated 19 bioclimatic variables with historical wildfire occurrence data, using 258 recorded natural fire locations from 2015 to 2025 as reference points. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis.Results and discussionAn Area Under the Curve (AUC) value of 0.711, which indicates moderate but acceptable predictive performance and is consistent with values reported in similar climate-driven susceptibility studies. The model results showed that the most influential drivers of fire susceptibility were temperature seasonality (BIO4), dry season precipitation (BIO17), and the minimum temperature of the coldest month (BIO6). According to future projections based on the Representative Concentration Pathways (RCP) 2.6 scenario using the Beijing Climate Center-Climate System Model Version 2-Medium Resolution (BCC-CSM2-MR) climate model for 2050 and 2070, the proportion of areas with high and extremely high fire susceptibility is projected to increase from 56.4% in 2025 to 64.0% in 2070, while low-and moderate-suscep¬tibility zones decline. This study provides one of the first climate-only, machine learning-based evaluations of present and future natural wildfire susceptibility in Mediterranean forests of Türkiye. These results revealed the escalating threat of wildfires in Mediterranean forests under climate change.
DOI: 10.3389/ffgc.2026.17718572026-03-18
Yuting Yin, Binyi Liu, Qing Chang
IntroductionUrban green spaces (UGSs) play a dual role in high-density cities: they are crucial for biodiversity conservation and serve as key venues for promoting residents’ health. However, the benefits gained by people in these settings depend less on objectively measured biodiversity and more on their subjective perception of it. Visual cues, which account for over 80% of sensory input, significantly shape this perception.MethodsThis study explores the interrelationships among measured plant diversity, perceived plant diversity, and visual landscape characteristics in UGSs, so that to develop a predictive model for perceived diversity using the other two variables. Based on a case study of nine representative parks in Shanghai, the research compared measured plant diversity—using four indices (arbor, shrub, herbaceous, and community diversity)—with perceived plant diversity and evaluated the influence of visual landscape features.ResultsResults showed a significant mismatch between measured and perceived diversity. Furthermore, visual characteristics were more effective than measured biodiversity in predicting perceived plant diversity. These findings offer practical, short-term design strategies for enhancing perceived biodiversity in urban parks—complementing longer-term ecological measures aimed at increasing actual biodiversity.DiscussionThis study advances the understanding of how objective biodiversity, human perception, and visual environment interact, and supports the design of nature-based solutions that benefit both human well-being and biodiversity conservation.
DOI: 10.3389/ffgc.2026.17321882026-03-18
Lei Gao, Hui Xiang, Wenjun Liu
Global warming driven by carbon dioxide (CO2) emissions is a major global concern. Forests play a vital role in climate mitigation as significant carbon sinks, and avoiding deforestation and forest degradation remains essential for limiting atmospheric CO₂. However, it remains unclear whether minimizing the use of plant-derived biomass necessarily maximizes climate benefits. We present a conceptual Perspective synthesizing existing literature and argue that forests should be viewed not only as static carbon reservoirs but also as dynamic systems that continuously sequester carbon through photosynthesis. Where forest area, ecological integrity, biodiversity, and soil stability are maintained, moderate and well-regulated use of plant-based materials—particularly for durable, non-combustion applications—may extend the residence time of biogenic carbon within the human economy and complement in situ forest carbon storage. We emphasize that carbon represents only one dimension of sustainability. Strategies aimed at increasing storage of plant photosynthetic products must consider trade-offs involving soil carbon, nutrient cycling, biodiversity, hydrology, and non-CO₂ greenhouse gas dynamics. This Perspective aims to stimulate discussion on evaluating plant-derived carbon storage within integrated, multi-objective sustainability frameworks rather than pursuing carbon maximization alone.
DOI: 10.3389/ffgc.2026.16927992026-03-13
Yann Emmanuel Miassi, Nancy Gélinas, Kossivi Fabrice Dossa, Idiatou Bah
The circular economy is now being presented as a promising way to respond to the increasing depletion of forest resources. However, its application remains limited in value chains in sub-Saharan Africa. This study aims to analyses the perception and acceptability of four circular approaches proposed to actors in the timber and forestry sector in Benin, to assess their potential for implementation in a local context. The research is based on a field survey conducted in the north and south of the country, using semi-structured interviews with direct and indirect actors in the sector. The data collected was subjected to qualitative analysis, supported by descriptive statistics and econometric models to achieve the study’s objectives. Four circular economy strategies were proposed: eco-design, focused on sustainable product design; optimization of operations, aimed at improving process efficiency while taking ecological criteria into account; loan-exchange, which encourages the pooling of resources between actors; and industrial ecology, focused on inter-company coordination for systemic flow management. The results show overall favorable acceptability, with a marked preference for eco-design, optimization, and industrial ecology, which are perceived as particularly beneficial from an environmental standpoint. The loan-exchange strategy stands out for its social roots, strengthening solidarity and community cooperation. However, the use of these strategies remains dependent on factors such as the size and legal status of companies, access to information, the profile of stakeholders, and the local context.
DOI: 10.3389/ffgc.2026.17558932026-03-13
Tahsin Çetin
This study investigated the suitability of rosemary (Rosmarinus officinalis) plant extract and hydrosol for use as a preservative in Scots pine (Pinus sylvestris l.) and Turkish beech (Fagus orientalis l.) wood. The air-dry and fully dry specific gravity, tensile-shrinkage, water absorption-expansion (swelling) properties, and retention quantities of the impregnated samples were determined and statistically evaluated. Experimental applications were carried out using short, medium and long-term immersion methods; samples were soaked in water for 6, 12, 24, 48, 72, and 96 h and performance analyses were performed. The findings revealed that tree species was a statistically significant and decisive factor on specific gravity values (F = 15.013; p < 0.001). Turkish beech wood (Mean = 0.63) exhibited higher specific gravity values compared to Scots pine wood (Mean = 0.53). While the impregnation period and some factor interactions were found to be significant on air-dry specific gravity, it was determined that the effect of variables other than tree species was limited in terms of fully dry specific gravity. A significant effect of time (F = 97.764; p < 0.001), concentration (F = 12.627; p < 0.001) and impregnation time (F = 11.713; p < 0.001) factors was detected in the tensile-shrinkage behavior. In particular, it was found that hydro-sol and hydro-sol+mordant applications increased dimensional stability by reducing tensile-shrinkage values. The dominant effect of tree species (F = 271.081; p < 0.001) and time (F = 169.730; p < 0.001) on water absorption properties was noteworthy; Scots pine (67.26%) was found to have a higher water absorption rate than Turkish beech (48.87%). Among the applications, a 10% hydrolysate concentration increased the water uptake rate to 60.92%, whereas the extract+mordant combination yielded more balanced results at 55.93%. It was determined that expansion (swelling) values were particularly affected by tree species, concentration, and time factors; extract-based applications tended to increase expansion, while hydrosol applications provided more limited dimensional changes. It was found that a 10% hydrosol application yielded an average swelling value of 4.26%, representing a reduction of approximately 22% compared to the control group’s value of 5.51%. Tree species was a determining factor in terms of retention values; results ranged from 0.34–1.62% in Scots pine and 0.13–0.73% in Turkish beech. In conclusion, rosemary hydrosol demonstrated more favorable performance in terms of dimensional stability in wood materials, while extract applications were found to have enhancing effects on certain physical properties. The findings suggest that rosemary extract and hydrosol could be considered as environmentally friendly alternatives in the ecological wood preservation industry, particularly for interior applications.
DOI: 10.3389/ffgc.2026.17923252026-03-09
Byungwoo Chang, Wontaek Lim, Dongwook W. Ko, Wanmo Kang, Kwangil Cheon
Forest mapping is essential for sustainable forest management and climate adaptation, enabling the assessment of forest composition and condition to prevent degradation. This study developed a U-Net-based deep learning framework for forest type classification using Sentinel-2 MSI satellite imagery and vegetation indices that capture seasonal canopy properties. A two-step approach was adopted, first delineating forested areas and then classifying forest types into needleleaf, broadleaf, and mixed forests. The forest area classification model achieved an overall accuracy of 0.958 (Kappa = 0.916), confirming reliable separation of forest and non-forest areas. For forest type classification, incorporating multi-seasonal imagery consistently enhanced performance, with the NDVI-based model achieving the highest overall accuracy of 0.831 (Kappa = 0.698). These results highlight the importance of integrating multi-seasonal spectral information to capture canopy variability and improve classification accuracy. The resulting reproducible framework thus supports ecosystem monitoring, hazard assessment, and adaptive forest management, offering foundational data for near real-time resource management under changing climatic conditions.
DOI: 10.3389/ffgc.2026.1768700