Tool For Forest Management Research Articles

A review of optimization and decision models of prescribed burning for wildfire management.

Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial-allocation, spatial-extent, temporal-only, and spatial-temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial-allocation and spatial-temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade-offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long-term impacts. Enhancing these models' accuracy and applicability will enable decision-makers to better manage wildfire treatmentoutcomes.

Enhanced Forest Inventories: A QGIS Plugin to incorporate R processing tools in forest management

Enhanced Forest Inventories: A QGIS Plugin to incorporate R processing tools in forest management

Variations on the maximum density-size lines to climate and site factors for Larix spp. plantations in northeast China

The maximum density-size line (MDSL) is a valuable tool in sustainable forest management, as it shows the relationship between site occupancy measures and mean tree size on a log-log scale. However, the responses of MDSLs to different climate and site variables still need to be clarified. Thus, this study aimed to assess the potential effects of various climate- and site-related factors on the slopes and intercepts of MDSLs for Larix spp. plantations in northeast China. The parameters of MDSLs were estimated using stochastic frontier regression (SFR) with three different error distribution assumptions, namely half-normal distribution (HN), exponential distribution (ED), and truncated-normal distribution (TN). Spatial distributions of maximum stand density index (SDImax) were mapped under different climate scenarios (RCP 8.5, RCP 4.5, and RCP2.6). The results revealed that the slopes on MDSLs without covariates were significantly shallower than Reineke's slope (−1.605), ranging from −1.2485 to −1.2026. Of the 22 covariates considered, 13 variables on SFR-HN and SFR-TN and 16 variables on SFR-ED had significant influences on MDSLs. The optimal MDSL model, including mean annual temperature (MAT) and soil pH as covariates using a HN assumption, decreased the Akaike's Information Criterion (AIC) by approximately 7.76%. The results also indicated that increasing MAT significantly reduced the maximum stand density for stands with a natural logarithm of quadratic mean diameter [ln(QMD)] below 2.6, while consistent increases were observed over the entire ln(QMD) range for soil pH. Moreover, the mean SDImax within the whole region increased significantly from 15.04% under RCP4.5 to 27.78% under RCP8.5. These findings emphasize the significant influences of climate and site conditions on the MDSL, thereby calibrating on traditional density management strategies may contribute significantly on carbon sequestration capacity of forests in the face of climate change.

Forest assisted migration and adaptation plantings in the Northeastern US: perspectives and applications from early adopters

Threats to the future function of forested ecosystems and stability of ecosystem service provisioning due to global change have motivated climate-adaptive forest management strategies that include various forms of tree planting termed “adaptation plantings”. Despite the emergence of these strategies, less is known as to how foresters and other natural resource managers perceive or are engaged with adaptation plantings like forest assisted migration (FAM). This knowledge gap is most pronounced in regions like New England and the North Central US (hereafter, the Northeastern US) where tree planting is less common but expected to be an important forest management tool for adaptation. To address this, we surveyed 33 natural resource managers in this region actively engaged in climate change adaptation (i.e., early adopters of the practice) to assess how tree planting for adaptation is currently being pursued against the perceived barriers, opportunities, and potential future engagement with the strategy. Survey respondents overwhelmingly (93.5%) forecast increases in the future use of adaptation plantings in their work in the region, attributed to increased awareness, acceptance, and interest in the practice. Respondents expressed notable interest in strategies related to diversification and most types of FAM (e.g., assisted population expansion and assisted range expansion), but hesitancy to engage with more contentious planting types like afforestation or FAM linked to the long-distance translocation of exotic species (e.g., assisted species migration). Although examples of local enrichment plantings (i.e., non-FAM) proliferate, nineteen of the top twenty most common tree genera planted contain at least one example of FAM in the study region. The most notable barriers reported were themed as 1) biotic and abiotic, 2) information and material, and 3) policy, social, and economic factors. While most respondents report difficulty obtaining adequate planting material from nurseries (i.e., seedlings), over 80% placed orders shortly before planting (< 1 year) which likely generates difficulty in sourcing seedlings suited for a specific site and future range of environmental conditions. Although this study is limited by focusing on subset of natural resource managers who are early adopters of climate change adaptation within the region, valuable inferences into the barriers and trends are possible from this population serving on the front lines of forest adaptation. Together, these results from early adopters suggest a potentially growing need for allocating resources that engage forest stewards in adaptation planning and serve to refine policy, financing, and management practices to support this adaptation strategy in this region and beyond.

Climate risk maps as boundary objects for future forests

Climate change poses significant threats to ecosystems and biodiversity. Conventional management strategies often fall short, leading to uncertainties in addressing these challenges. Natural and environmental scientists play a crucial role by providing evidence-based guidance. Social science research, at the same time, highlights the complexity of transferring and applying knowledge across different social and professional groups and shows that further research is needed. Using a German case study, my research addresses this issue by examining the dynamics between predictive climate risk maps, intended as decision-support tool for forest management, the developing scientists, the receiving environmental managers and further political actors. Semi-structured qualitative interviews with representatives from these groups were conducted and analyzed, revealing that climate risk maps can function as predictive boundary objects, balancing flexibility and robustness. With their high level of visual and epistemic power, these maps generate knowledge tensions, facilitate interactions, and foster the implicit co-production of broader environmental management discourse. At the same time the maps are continuously contested, discussed, and updated through feedback, becoming themselves part of an ongoing informal co-productive process. This dual role creates ambiguity: they provide concrete answers to specific management related questions while highlighting simultaneously limitations that prompt more fundamental inquiries, driving an overall societal learning process. Hence, future efforts should enhance formal support for co-productive processes to ensure evidence-based advisory tools are scientifically robust, contextually adapted, and democratize knowledge dynamics through continuous dialogue, mutual learning, and integration of scientific as well as local knowledge.

Economic valuation of carbon sequestration in Quito’s Metropolitan Park using Sentinel-2 and neural networks

This study introduces an innovative method for assessing the economic value of carbon stored by the urban park forest in Quito, Ecuador. The method uses Sentinel-2 remote sensing data and advanced deep learning techniques. A large forest study area was chosen, and detailed field measurements were taken to gather biomass and carbon data. Sentinel-2 satellite images were processed to correct for radiation and atmospheric conditions, and vegetation indices such as NDVI and EVI were calculated. To model forest biomass, a convolutional neural network (CNN) was created and trained using Sentinel-2 spectral bands and vegetation indices. The model was validated with independent field data and demonstrated high accuracy in estimating biomass and carbon, as indicated by evaluation metrics like RMSE and R². The findings include detailed maps showing the spatial distribution of biomass and carbon in the study area, which can be a valuable tool for forest management and the implementation of policies for valuing stored carbon. This approach combines the high resolution of Sentinel-2 data with the predictive power of neural networks, providing a robust and scalable method for estimating carbon across large forest areas. The study's conclusions emphasize the feasibility and accuracy of this approach and its potential for application in various forestry and geographical contexts.

FBA-DPAttResU-Net: Forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images

Forest burned area (FBA) detection using remote sensing (RS) data is critical for timely forest management and recovery attempts after wildfires. This study introduces a dual-path attention residual-based U-Net (DPAttResU-Net), a novel end-to-end deep learning (DL) model tailored for FBA detection using dual-source post-fire Sentinel-1 (S1) and Sentinel-2 (S2) satellite RS imagery. To better distinguish FBAs from other land cover types, DPAttResU-Net incorporates a dual-pathway structure to exploit complementary geometrical/physical and spectral features from S1 and S2, respectively. An integral component in the proposed architecture is the channel-spatial attention residual (CSAttRes) block, which emphasizes salient features through the channel and spatial attention modules, thus improving the burned area feature representation. To compare DPAttResU-Net to state-of-the-art DL models, experiments were conducted on benchmark FBA datasets collected over 12 areas, where ten datasets were used as training data and two datasets were used to test the trained DL models. The experimental results demonstrate the high proficiency of the proposed deep model in meticulously delineating FBAs. In further detail, DPAttResU-Net, with a PFN of 17.96 (%) in the first case and an IoU of 89.31 (%) in the second case, outperformed the existing U-Net-based models. Accordingly, through dual-path integration and attention mechanisms, DPAttResU-Net contributes to accurately identifying FBAs by preserving their geometrical details, making it a promising tool for post-wildfire forest management.

The role of Participatory Forest Management for forest diversity enhancement in Ethiopia

Ethiopia is rich in the flora of tropical Africa and this situation enables the center of origin for plant species diversity. Despite the forest resource, quality is declining from time to time at an alarming rate and become major problem during the second half of the 20th century. Yet, no studies about the role of PFM in comparison with the None-PFM forests for forest diversity status and sustainability. PFM was introduced for sustainable forest management involving the local communities as part of decision maker on the forest resources utilization and conservation practices by empowered women`s for different actions and participation using forest resources wisely without hampering the next generation. Therefore, this review conducted to have current information about the role of participatory forest management (PFM) for sustainable forest management in different part of Ethiopia. This review revealed that participatory forest management play appreciable role for forest sustainability that forest species diversity and density was increased along different locations of the country forests governed by PFM than None-PFM forests. This review was greatly provide crucial information for forest stake holders and policy makers that participatory forest management was unreplaceable tool for sustainable forest management using forest products wisely and sustainably without debilitating the next generation

Fire exclusion alters forest evapotranspiration: A comprehensive water budget analysis in longleaf pine woodlands

AbstractForests are critical to water resources, but high evapotranspiration (ET) can reduce water yield. Thinning and prescribed fire reduce forest density and often reduce ET, promoting higher water yield. However, results from such treatments have been inconsistent, possibly because of unknown interactions among individual ET components. We compare water budget components of longleaf pine (Pinus palustris Mill.) woodlands with frequent prescribed fire to the water budget components of fire‐excluded stands. We hypothesized that fire exclusion would result in higher ET due to increased midstory transpiration (Et) and interception (Ei), and higher evaporation from litter (Ilitter). Reference plots were burned every two years while treatment plots had fire excluded for 15–20 years. Fire treatments were repeated in two sites representing a soil moisture gradient, noted as mesic and xeric. We measured woody Et using sap flux, and we modeled groundcover Et using physiological models. We measured Ei of canopy and groundcover layers, modeled Es litter biomass, and constructed a total component‐based water budget for each site and treatment. Compared with reference plots, midstory Et was 300%–800% higher in fire exclusion plots. Groundcover Et was ~80% less than reference treatments, countering the effects of midstory growth on total ET. Stand Ei followed similar trends, with groundcover Ei in reference plots countering the effects of midstory and litter Ei in fire exclusion plots. As expected, total ET in the xeric site was 18% higher in fire exclusion plots. However, ET in the mesic site was 16% lower in the fire exclusion plots due to high groundcover Et and Ei in reference plots. Thus, our results show that fire exclusion changes total forest ET, but the size and direction of the effect vary depending on the balance between midstory and groundcover transpiration and interception. These results highlight the importance of groundcover in ecosystem function in low‐density forests and may help explain inconsistent results from studies of water yields following thinning and fire. While prescribed fire is a valuable tool in forest management, we suggest that the effects of fire on ET are complex and require careful accounting of all water fluxes within a forest ecosystem.

Biotope Mapping of San Mariano, Isabela for Forest Conservation and Management

Abstract Forests in San Mariano, Isabela, Philippines are invaluable resources facing threats from destructive activities and natural disturbances, resulting to significant forest loss. To address this challenge, this study introduces biotope mapping as a tool for forest conservation and management in San Mariano, Isabela. The goal is to identify and map the different biotopes in San Mariano, Isabela which helps in enhancing sustainable forest management strategies, ensuring the long-term preservation of forest ecosystems. By conducting site visits, utilizing remote sensing techniques and geographic information systems (GIS) analysis, the distribution and classification of biotopes in San Mariano are determined. Through overlaying the soil type map, land cover map, and landform map, 22 biotopes were determined, and each were visually presented by showing a section-elevation diagram of each biotope. Furthermore, an assessment was also done for each biotope providing a comprehensive understanding of the different biotopes within the study area. This also serves as a reference for prioritizing conservation efforts, establishing protected areas, implementing habitat restoration initiatives, and mitigating threats to forest ecosystems. The findings and recommendations of this study contribute to the promotion of effective forest conservation and management, ensuring the long-term preservation of the forested landscapes.

Use of artificial neural networks with the physiological principles to predict growth model

Process-based models have become essential tools for forest planning and management due to their ability to assess the impacts of climate and operational changes on productivity. Physiological Principles Predicting Growth (3-PG) is a process-based/hybrid model widely used, associated with allometric models to estimate dendrometric variables. However, the accuracy of the model outputs still needs to be improved, especially those of interest to forest management. In this study, we tested the efficiency of artificial neural networks (ANNs) to estimate the average diameter at 1.30 m height (dbh), total height, stand volume, and root, stem, and foliage biomass. We also evaluated the prediction of two parameters that estimate the proportion of biomass allocated aboveground (ap and np). A database from published 3-PG parameterizations was generated and used to train the ANNs. Two networks were validated against observed data from two municipalities in the MG state of Brazil. Potential productivity maps at six and seven years were generated for Brazil, to evaluate the performance of the 3-PG + ANN model. All ANNs presented satisfactory statistical results in predicting the ap and np parameters, foliage biomass, dbh, total height and stand volume. The use of artificial neural networks integrated with the 3-PG model or another process-based model is a promising alternative to improve the accuracy of estimates, especially those of interest for forest management. Artificial neural networks offer a simple, flexible way to insert important variables into a process-based model. The parameters and 3-PG outputs can be estimated efficiently using artificial neural networks.

Machine learning applications in forest and biomass supply chain management: a review

ABSTRACT Forest and biomass crops for bioenergy and bioproducts can promote a sustainable bioeconomy while effectively reducing greenhouse gas (GHG) emissions to mitigate global warming. One of the most concerning issues is selecting and using appropriate modeling and analytical technologies to optimize the benefits of multi-feedstock biomass supply chains, including logistics. Machine learning (ML) has been used to solve increasingly complex supply chain problems, providing powerful tools for sustainable forest management and biomass resource development. Existing research is extensive and spans many different ML techniques, but synthesis is needed to help guide the adoption of these rapidly evolving tools. This review summarizes ML applications in forest and biomass supply chain management in terms of data, algorithms, and process examples, with an emphasis on direct application to supply chain management. ML is a viable technique to support strategic, operational, and tactical planning and decision-making in this field and can enhance the environmental and economic performance of diverse forest and biomass supply chains.

Tree-ring based forest model calibrations with a deep learning algorithm

Process-based forest growth models are important tools for forest management and studies for forest growth, carbon sequestration, and forest dynamics. However, their reliable simulations rely not only on the quality of model construction but also on accurate parameters to appropriately depict various physiological and biophysical processes in the models. While explicit physiological measurements are excellent sources for model parameterization, they are not always readily available. It would be ideal to use easy-to-measure tree-ring data as a benchmark for parametrization, but such applications have been rare. Here we present a new approach to reasonably parameterize a stand growth model based on tree-ring data using deep learning algorithms. We integrated a stable carbon isotope (δ13C) version of a simple process-based stand growth model, 3-PG, into a recurrent neural network (RNN). The new 3PG-RNN model trained the RNN network and calibrated 3-PG parameters to minimize the mean squared error between the 3-PG outputs and targeted values. We then tested 3PG-RNN based on two Abies grandis stands located in Northern Idaho, USA. The results showed that the 3PG-RNN model was efficient in calibrating key parameters related to gas-exchange processes, including specifically quantum yield, maximum canopy conductance, and the slope function for stomatal responses to vapor pressure deficit. These calibrated parameters provided reasonable simulations for gas-exchanges similar to those constrained by explicit physiological observations. This was particularly true when both long-term diameter growth (estimated from tree-ring width) and tree-ring δ13C were used for model training. The RNN tool made it possible to use tree ring data as the key benchmark to calibrate the forest model and provide unbiased simulations for gas exchanges and forest growth with the RNN approach, which would greatly facilitate forest studies and management.

Territorial planning support tool for sustainable forest management in the Amazon

In forest management, it is crucial to ensure process efficiency to maintain economic viability, especially in native forests where timber volume varies due to the uneven distribution of commercially valuable species. This variability poses a challenge for harvest planning, as the inherent heterogeneity of these forests often results in inconsistent productivity. This study employs the SOTER-PA system, an algorithm-based tool utilizing a minimalist tabu search, to achieve a more homogeneous territorial planning structure for better utilization of natural resources and increased economic returns from forestry activities. The study area is the Tapajós National Forest in the state of Pará, with a forest inventory database covering 2,218.7 hectares and 28,369 trees across 93 species. After applying exclusion criteria and data processing, 8,644 trees were deemed suitable for harvest. The tests suggested a new organization of Work Units, enabling a more balanced income distribution among them, and delineating the areas to be exploited in each cutting cycle. The results show that SOTER-PA not only improves the equitable distribution of natural resources but also enhances economic productivity. This improvement is attributed to the more homogeneous and optimized distribution of high-value species like red cedar and purpleheart. Furthermore, when comparing the SOTER-PA generated parcels with conventional management, the parcels created by SOTER-PA were more homogeneous, with better productivity quality and lower standard deviation, indicating greater uniformity. Thus, the use of SOTER-PA proves to be an efficient alternative for optimizing sustainable forest management planning, offering a more effective method for sustainable territorial planning in native forests, providing both economic and environmental benefits.

Whole-genome screening for near-diagnostic genetic markers for four western European white oak species identification

Key messageMining genome-wide DNA sequences enabled the discovery of near-diagnostic markers for species assignment in four European white oaks (Quercus petraea (Matt.) Liebl., Quercus pubescens Willd., Quercus pyrenaica Willd., and Quercus robur L.) despite their low interspecific differentiation. Near-diagnostic markers are almost fully fixed in one species and absent in the three others. As a result, only a handful of markers are needed for species identification, making this genetic assay a very promising operational taxonomic assignment procedure in research and forestry.ContextIdentifying species in the European white oak complex has been a long-standing concern in taxonomy, evolution, forest research, and management. Quercus petraea (Matt.) Liebl., Q. robur L., Q. pubescens Willd., and Q. pyrenaica Willd. are part of this species complex in western temperate Europe and hybridize in mixed stands, challenging species identification.AimsOur aim was to identify near-diagnostic single-nucleotide polymorphisms (SNPs) for each of the four species that are suitable for routine use and rapid diagnosis in research and applied forestry.MethodsWe first scanned existing whole-genome and target-capture data sets in a reduced number of samples (training set) to identify candidate diagnostic SNPs, i.e., genomic positions being characterized by a reference allele in one species and by the alternative allele in all other species. Allele frequencies of the candidates SNPs were then explored in a larger, range-wide sample of populations in each species (validation step).ResultsWe found a subset of 38 SNPs (10 for Q. petraea, 7 for Q. pubescens, 9 for Q. pyrenaica, and 12 for Q. robur) that showed near-diagnostic features across their species distribution ranges with Q. pyrenaica and Q. pubescens exhibiting the highest (0.876) and lowest (0.747) diagnosticity, respectively.ConclusionsWe provide a new, efficient, and reliable molecular tool for the identification of the species Q. petraea, Q. robur, Q. pubescens, and Q. pyrenaica, which can be used as a routine tool in forest research and management. This study highlights the resolution offered by whole-genome sequencing data to design near-diagnostic marker sets for taxonomic assignment, even for species complexes with relatively low differentiation.

Bayesian approach for modeling diameter distributions of patula pine (Pinus patula Schiede ex Schltdl. & Cham.) stands in Southern Mexico

Characterization of the diameter distribution of forest stands managed for timber production provides accurate estimates of existing wood volume by diameter class and area unit. It provides the basis for determining the stand's ecological and economic value, its structure, the potential for ecosystem services, and appropriate management practices. Patula pine is one of the most important conifers in southern Mexico and requires biometric tools to improve management strategies. This study aimed to use the Bayesian approach to estimate the parameters of Johnson's SB (JSB) and Weibull (W) probability density functions (pdf) and to model the diameter distribution of patula pine stands in southern Mexico. We used data from 462 diameter distributions derived from 66 permanent 400 m2 sampling plots. The results indicated that the Bayesian approach was appropriate for estimating the JSB and W pdf parameters. Due to the similarity between the nonparametric tests and the goodness-of-fit statistics of the functions, the graphical analysis helped to select the W function as the better option to adequately model the diameter distribution of the analyzed stands. The equations developed for parameter prediction of the W function with stand variables allowed us to characterize and project the theoretical distribution of diameter classes in a simple accurate way with variables easily obtained from forest inventories. The Bayesian approach proved to be an effective method for estimating the pdf parameters used. This allowed for the correct characterization of the observed diameter distribution, and its implementation was easy for executing the procedure. Diameter distribution models are a powerful tool for forest inventory and management of patula pine stands analyzed in southern Mexico.

An Eco-Dendrometric Study of Atlas Cedar (Cedrus Altantica Manetti) from Theniet-El-Had National Park (North West of Algeria)

Purpose: This work contributes to the study of the growth pattern of Atlas Cedar (characterized by dendrometric variables) relevant to the stands from Pepinière canton in relation to site factors (ecological and pedology descriptors). Theoretical framework: A forest ecosystem is a complex set of external factors that have a positive or negative effect on tree growth. Species ecology is a key point in understanding their behavior in relation to environmental fluctuations. Method: Forty-six temporary plots were set up to collect ecological, pedological, and dendrometric data, allowing for a characterization of the growth pattern of Atlas Cedar stands. Results and conclusion: The study highlights the presence of an unmanaged irregular forest. Despite being located in an environment conducive to its growth, this forest is subject to degrading anthropogenic activity. Analysis of variance at a significance level of 95% shows that altitude and limestone content are the main site factors influencing Atlas Cedar growth. Research implications: knowledge of the growth rate of this species allows better silvicultural operations, and application of more adequate management plans for this endemic protected species. Originality/value: This study constitutes a reliable scientific tool for forest managers, particularly in national reforestation plans, which are doomed to failure due to a lack of knowledge of the behavior of the species used.

Impact of prescribed fire on soil microbial communities in a Southern Appalachian Forest clear-cut.

Escalating wildfire frequency and severity, exacerbated by shifting climate patterns, pose significant ecological and economic challenges. Prescribed burns, a common forest management tool, aim to mitigate wildfire risks and protect biodiversity. Nevertheless, understanding the impact of prescribed burns on soil and microbial communities in temperate mixed forests, considering temporal dynamics and slash fuel types, remains crucial. Our study, conducted at the University of Tennessee Forest Resources AgResearch and Education Center in Oak Ridge, TN, employed controlled burns across various treatments, and the findings indicate that low-intensity prescribed burns have none or minimal short-term effects on soil parameters but may alter soil nutrient concentrations, as evidenced by significant changes in porewater acetate, formate, and nitrate concentrations. These burns also induce shifts in microbial community structure and diversity, with Proteobacteria and Acidobacteria increasing significantly post-fire, possibly aiding soil recovery. In contrast, Verrucomicrobia showed a notable decrease over time, and other specific microbial taxa correlated with soil pH, porewater nitrate, ammonium, and phosphate concentrations. Our research contributes to understanding the intricate relationships between prescribed fire, soil dynamics, and microbial responses in temperate mixed forests in the Southern Appalachian Region, which is valuable for informed land management practices in the face of evolving environmental challenges.

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

AbstractThe escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: A Bayesian Network Modeling Approach

Wildfire occurrences have increased and are projected to continue increasing globally. Strategic, evidence-based planning with diverse stakeholders, making use of diverse ecological and social data, is crucial for confronting and mitigating the associated risks. Prescribed fire, when planned and executed carefully, is a key management tool in this effort. Assessing where prescribed fire can be a particularly effective forest management tool can help prioritize efforts, reduce wildfire risk, and support fire-resilient lands and communities. We collaborated with expert stakeholders to develop a Bayesian network model that integrated a large variety of biophysical, socioecological, and socioeconomic spatial information for the Southeastern United States to quantify where risk is high and where prescribed fire would be efficient in mitigating risk. The model first estimated wildfire risk based on landscape-scale interactions among the likelihoods of fire occurrence and severity and the people and resources potentially exposed—accounting for socioeconomic vulnerabilities as well as key ecosystem services. The model then quantified the potential for risk reduction through prescribed fire, given the existing fuel load, climate, and other landscape conditions. The resulting expected risk estimates show high risk concentrated in the coastal plain and interior highland subregions of the Southern US, but there was considerable variation among risks to different ecosystem services and populations, including potential exposure to smoke emissions. The capacity to reduce risk through fuel reductions was spatially correlated with risk; where these diverged, the difference was largely explained by fuel load. We suggest that both risk and the capacity for risk reduction are important in identifying priorities for management interventions. The model serves as a decision support tool for stakeholders to coordinate large-landscape adaptive management initiatives in the Southern US. The model is flexible with regard to both empirical and expert-driven parameterizations and can be updated as new knowledge and data emerge. The resulting spatial information can help connect active management options to forest management goals and make management more efficient through targeted investments in priority landscapes.