Search Results (3,092)

LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 µs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment. Full article

Because wildfire spread is strongly influenced by instantaneous gusts, models that use only mean wind speed typically underestimate spread. In contrast, incorporating suppression effects often leads to overestimation. To reduce these errors, this paper newly proposes the concepts of an effective wind speed (EWS) and an EWS coefficient that jointly account for short-range forecast mean wind speed and the maximum gust from numerical weather prediction. The EWS is defined as an EWS coefficient-weighted average of the mean wind speed and maximum gust, so that the simulated perimeter matches the observed wildfire perimeter as closely as possible. Here, EWS refers exclusively to near-surface horizontal wind speed; vertical wind components are not considered. The EWS coefficient is modeled as a function of elapsed time since ignition, thereby implicitly reflecting the level of suppression resource deployment. The proposed frameworks are described in detail using time-stamped perimeters from multiple large-scale wildfires that occurred concurrently in South Korea during a specific period. On this basis, an EWS coefficient suitable for operational use in South Korea is derived. Using the derived EWS for spread prediction, the Sørensen index increased by up to 0.4 compared with predictions based on maximum gust alone. Incorporating the proposed EWS and coefficient into Korean wildfire spread simulators can improve the accuracy and robustness of predictions under extreme weather conditions, supporting safer and more efficient wildfire response. Full article

Forest fire monitoring in remote sensing imagery has long relied on traditional perception models that primarily focus on detection or segmentation. However, such approaches fall short in understanding complex fire dynamics, including contextual reasoning, fire evolution description, and cross-modal interpretation. With the rise of multi-modal large language models (MLLMs), it becomes possible to move beyond low-level perception toward holistic scene understanding that jointly reasons about semantics, spatial distribution, and descriptive language. To address this gap, we introduce FireMM-IR, a multi-modal large language model tailored for pixel-level scene understanding in remote-sensing forest-fire imagery. FireMM-IR incorporates an infrared-enhanced classification module that fuses infrared and visual modalities, enabling the model to capture fire intensity and hidden ignition areas under dense smoke. Furthermore, we design a mask-generation module guided by language-conditioned segmentation tokens to produce accurate instance masks from natural-language queries. To effectively learn multi-scale fire features, a class-aware memory mechanism is introduced to maintain contextual consistency across diverse fire scenes. We also construct FireMM-Instruct, a unified corpus of 83,000 geometrically aligned RGB–IR pairs with instruction-aligned descriptions, bounding boxes, and pixel-level annotations. Extensive experiments show that FireMM-IR achieves superior performance on pixel-level segmentation and strong results on instruction-driven captioning and reasoning, while maintaining competitive performance on image-level benchmarks. These results indicate that infrared–optical fusion and instruction-aligned learning are key to physically grounded understanding of wildfire scenes. Full article

Prescribed fire is a common habitat management tool for white-tailed deer (Odocoileus virginianus Zimm.) that can influence browse quantity and quality. We tested effects of time since burn and number of burns within a decade on browse forage productivity in forested stands in the Pineywoods ecoregion of Texas. We utilized 46 plots on sites managed by the United States Forest Service National Forests and Grasslands in Texas, The Nature Conservancy, and a private landowner. Preferred browse forage species were sampled and analyzed for nutrient content, and years since last prescribed burn and the number of burns within the last 10 years were compared. Deer had strong preferences for plants with greater crude protein, magnesium, and potassium. Crude protein and net energy for maintenance were generally greater with a more frequent burn regime. Different nutrients peaked at different burn intervals. Frequent fires resulted in higher crude protein ($\overline{\mathrm{x}}$ = 14.0%) than infrequently burned sites. At four burns per decade, net maintenance energy was highest ($\overline{\mathrm{x}}$ = 0.6 Mcal Kg⁻¹). Linear regression models only explained between 28% and 41% of utilization, although some preferences for some nutrients, such as crude protein and magnesium, were detected. To improve the nutritional carrying capacity for white-tailed deer, long-term management regimes should incorporate site-specific burn plans that include fire frequency. Timing and burn frequency are critical to achieving optimum results that improve browse forage availability, quality, and utilization. Full article

Wildfires alter multiple soil functions in forest ecosystems, but how they reconfigure the linkages between these functions is not fully understood. We evaluated the 1-year-postfire and 11-year-postfire effects of wildfire on carbon sequestration, nutrient cycling, fertility maintenance, and erosion regulation, as well as their relationships, in a Chinese boreal larch forest. We further identified the environmental drivers regulating these associations. One year postfire, the soil fertility index transiently increased by 85%, whereas the carbon sequestration and nutrient cycling declined by 58% and 54%, respectively. Principal component analysis showed that wildfire decoupled the multivariate relationships between four soil functions. While these functions were closely clustered in unburned controls, they became dispersed one year postfire, indicating functional dissociation. After eleven years of recovery, a partial reassembly occurred, but with a reconfigured functional structure distinct from the pre-fire state. For the functional pairs, the impact of wildfire was limited to shifting the relationship between the soil fertility and nutrient cycling from a non-significant negative correlation to a significant positive correlation. Redundancy analysis showed that the soil water content remained the primary environmental driver of soil functional relationships before and after the fire, but its role reversed from negative in unburned stands to positive during the postfire recovery, suggesting a shift toward water-mediated functional coupling. Wildfires in boreal forests have far-reaching effects on soil ecosystems, including impacts on the relationships between various soil functions. Our results indicate that wildfire reconfigures the network of soil function linkages in boreal forests, with implications for the recovery of boreal soil ecosystems. Full article

The dead fine fuel moisture content (DFFMC) directly affects forest fire occurrence and spread. Accurate DFFMC prediction is key to estimating forest fire risk and behavior. The well-fitting machine learning (ML)-based meteorological factor regression models are a focus of DFFMC prediction modeling. Nevertheless, this method’s reliance on a considerable amount of training data and limited extrapolation hinders its potential for extensive implementation in practice. To improve the prediction accuracy of the model in the context of limited training data volumes and interspecies and spatial extrapolated predictions, this study proposed a novel DFFMC prediction method based on a knowledge-embedded neural network (KENN). By integrating the partial differential equation (PDE) of the meteorological response of forest fuel moisture content into a multilayer perceptron (MLP), the KENN utilizes prior physical knowledge and posterior observational data to determine the relationship between meteorology and moisture content. Data from Mongolian oak, white birch, and larch were collected to evaluate model performance. Compared with three representative ML algorithms for DFFMC prediction—random forest (RF), long short-term memory networks (LSTM), and MLP—the KENN can efficiently reduce training data volume requirements and improve extrapolation prediction accuracy within the investigated fire season, thereby enhancing the usability of ML-based DFFMC prediction methods. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

Forest fire susceptibility prediction is essential for effective management, yet considerable uncertainty persists under future climate change, especially in climate-sensitive plateau regions. This study integrated MODIS fire data with climatic, topographic, vegetation, and anthropogenic variables to construct an Extreme Gradient Boosting (XGBoost) model for the Yunnan Plateau, a region highly prone to forest fires. Compared with Support Vector Machine and Random Forest models, XGBoost showed superior ability to capture nonlinear relationships and delivered the best performance, achieving an AUC of 0.907 and an overall accuracy of 0.831. The trained model was applied to climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 to assess future fire susceptibility. Results indicated that high-susceptibility periods primarily occur in winter and spring, driven by minimum temperature, average temperature, and precipitation. High-susceptibility areas are concentrated in dry-hot valleys and mountain basins with elevated temperatures and dense human activity. Under future climate scenarios, both the probability and spatial extent of forest fires are projected to increase, with a marked expansion after 2050, especially under SSP5-8.5. Although the XGBoost model demonstrates strong generalizability for plateau regions, uncertainties remain due to static vegetation, coarse anthropogenic data, and differences among climate models. Full article

Forests play a central role in climate change mitigation by acting as biogenic carbon reservoirs and providing renewable biomass for energy systems. In Portugal, where fire-prone landscapes and species composition dynamics pose increasing management challenges, understanding the carbon storage potential of forest biomass is crucial for designing effective decarbonization strategies. This study provides a comprehensive characterization of the Portuguese forest and quantifies the biogenic carbon stored in live and dead biomass across the main forest species. Species-specific carbon contents, rather than the conventional 50% assumption widely used in the literature, were applied to National Forest Inventory data, enabling more realistic and representative carbon stock estimates expressed in kilotonnes of CO₂ equivalent. While the approach relies on inventory-based biomass data and literature-derived carbon fractions and is therefore subject to associated uncertainties, it provides an improved representation of species-level carbon storage at the national scale. Results show that Pinus pinaster, Eucalyptus globulus, and Quercus suber together represent the largest share of carbon storage, with approximately 300,000 kilotonnes of CO₂ equivalent retained in living trees. Wood is the dominant carbon pool, but roots and branches also account for a substantial fraction, emphasizing the need to consider both above- and below-ground biomass in carbon accounting. In parallel, a bibliometric analysis based on the systematic evaluation of scientific publications was conducted to characterize the evolution, thematic focus, and geographic distribution of global research on forest-based biogenic carbon. This analysis reveals a rapidly expanding scientific interest in biogenic carbon, particularly since 2020, reflecting its growing relevance in climate change mitigation frameworks. Overall, the results underscore both the strategic importance of Portuguese forests and the alignment of this research with the broader international scientific agenda on forest-based biogenic carbon. Full article

Interfaces of rather different natures—as, e.g., bacterial colony or forest fire boundaries, or semiconductor layers grown by different methods (MBE, sputtering, etc.)—are self-affine fractals, and feature scaling with universal exponents (depending on the substrate’s dimensionality d and global topology, as well as on the driving randomness’ spatial and temporal correlations but not on the underlying mechanisms). Adding lateral growth as an essential (non-equilibrium) ingredient to the known equilibrium ones (randomness and interface relaxation), the Kardar–Parisi–Zhang (KPZ) equation succeeded in finding (via the dynamic renormalization group) the correct exponents for flat $d=1$ substrates and (spatially and temporally) uncorrelated randomness. It is this interplay which gives rise to the unique, non-Gaussian scaling properties characteristic of the specific, universal type of non-equilibrium roughening. Later on, the asymptotic statistics of process $h\left(x\right)$ fluctuations in the scaling regime was also analytically found for $d=1$ substrates. For $d>1$ substrates, however, one has to rely on numerical simulations. Here we review a variational approach that allows for analytical progress regardless of substrate dimensionality. After reviewing our previous numerical results in $d=1$, 2, and 3 on the time evolution of one of the functionals—which we call the non-equilibrium potential (NEP)—as well as its scaling behavior with the nonlinearity parameter $\lambda$, we discuss the stochastic thermodynamics of the roughening process and the memory of process $h\left(x\right)$ in KPZ and in the related Golubović–Bruinsma (GB) model, providing numerical evidence for the significant dependence on initial conditions of the NEP’s asymptotic behavior in both models. Finally, we highlight some open questions. Full article

The caldén (Neltuma caldenia) forest, a xerophytic low-stature ecosystem in central Argentina, faces increasing threats from land use change and desertification. This study assesses the capability of full-polarimetric L-band SAR data from the Argentine SAOCOM-1A satellite to characterise forest attributes in this ecosystem. We computed the Generalised Radar Vegetation Index (GRVI) and compared it with aboveground biomass and tree canopy cover data from the Second National Forest Inventory, under fire and non-fire conditions. We also assessed other SAR indices and polarimetric decompositions. GRVI values exhibited limited variability relative to the broad range of field-estimated biomass, and most regression models were not statistically significant. Nevertheless, GRVI effectively distinguished woody from non-woody vegetation and showed a weak correlation with canopy cover. Statistically significant, albeit weak, correlations were also observed between biomass and specific polarimetric components, such as the helix term of the Yamaguchi decomposition and the Pauli volume component. Key challenges included limited spatial and temporal coverage of SAOCOM-1A data and the distribution of field plots. Despite these limitations, our results support the use of GRVI for land cover monitoring in semiarid regions, emphasising the importance of multitemporal data, integration with C-band SAR, and enhanced field sampling to improve forest attribute modelling. Full article

Nowadays, optimization methods are widely used to adjust controller parameters and tune their optimal values in order to enhance the efficiency and performance of dynamic systems. In this study, the parameters of a linear Proportional–Integral (PI) controller were optimized by using five different optimization algorithms, such as Artificial Tree Algorithm (ATA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DEA), Constrained Multi-Objective State Transition Algorithm (CMOSTA), and Adaptive Fire Forest Optimization (AFFO). The optimized controllers were implemented in real time for temperature control of a Heat-flow System (HFS) under various step and time-varying reference signals. In addition, the Ziegler–Nichols (Z–N) method was also applied to the system as a benchmark to compare the temperature tracking performance of the proposed optimization methods. To further evaluate the performance of each optimization algorithm, Mean Absolute Error (MAE) values were calculated, and improvement ratios were obtained. The experimental results showed that the proposed optimization methods provided more successful reference tracking and enhanced controller performance as well. Full article

In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart forestry. By connecting sensors, drones and satellites, IoT allows for continuous monitoring of forest ecosystems, risk anticipation and decision optimization in real-time. The purpose of this study is to perform a comprehensive narrative analysis of the relevant scientific literature from the recent period (2020–2025) regarding the application of IoT in forestry, highlighting the conceptual, technological and institutional developments. Based on a selection of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (29 full-text articles), four major axes are analyzed: (A) forest fire detection and prevention; (B) climate-smart forestry and carbon accounting; (C) forest digitalization through the concepts of Forest 4.0, Forest 5.0 and Digital Twins; (D) sustainability and digital forest policies. The results show that IoT is a catalyst for the sustainable transformation of the forest sector, supporting carbon accounting, climate-risk reduction and data-driven governance. The analysis highlights four major developments: the consolidation of IoT–AI architectures, the integration of IoT and remote sensing, the emergence of Forest 4.0/5.0 and Digital Twins and the growing role of governance and data standards. These findings align with the objectives of the EU Forest Strategy 2030 and the European Green Deal. Full article

The northern Brazilian Amazon has ecological transition ecosystems with high diversity and endemism of tree species and few botanical collections. We evaluated the phytosociology between Dense Ombrophilous Forest (Ds) and Forested Campinarana (Ld) within Anauá National Forest in Roraima, Brazil. A total of 14,730 trees with a DBH ≥ 10 cm were inventoried across 30 hectares (ha), distributed among 55 botanical families, 183 genera, 386 species, and 123 undetermined trees. Ten hyperdominant tree families accounted for 69% of the sampled trees and 65% of the stored forest carbon (102.9 ± 5.0 Mg ha⁻¹), like Arecaceae (2555 trees), Fabaceae (1738 trees), and Sapotaceae (1311 trees). Ten hyperdominant species accounted for 32% of the sampled individuals and 32% of the stored forest carbon (46.3 ± 3.8 Mg ha⁻¹), like Euterpe precatoria (1151 trees), Pouteria macrophylla (561 trees) and Inga alba (574 trees). Anauá National Forest has great potential for sustainable multiple-use forest management through forest concessions; however, tree mortality due to natural causes and anthropogenic actions (deforestation, illegal selective logging, and forest fires) was considered high (7%) for tropical forests in the Amazon. Full article

This article analyzes prohibitions against the destruction of flora in the calendrical regulations of the late Western Han period and in the millenarian cosmological discourses in the Taiping jing 太平經 (Canon of Great Peace). The study focuses on the “Zhaoshu sishi yueling wushi tiao” 詔書四時月令五十條 (“Edict of Monthly Ordinances for the Four Seasons in Fifty Articles”) which was promulgated by Wang Mang in 5 CE. The Edict prohibited setting fire to forests and was intended to restore cosmic harmony. At the time, natural disasters and celestial anomalies were interpreted as signs of the loss of the Mandate of Heaven. Heavenly patterns and hemerology play a central role here by enabling environmental regulations to be incorporated into a political logic of legitimization. The Canon of Great Peace reinterprets these norms by replacing seasonal cycles with an interpretation of balance between yin and yang and by giving environmental prohibitions eschatological significance. Thus, calendrical regulations for natural resource management transform into an apocalyptic discourse in which the natural environment becomes the setting for cosmic disorder that must be avoided. Full article