Search Results (45)

Sustainable Forest Management (SFM) requires the building of relationships among diameter increment, shape, and size (ISS), and increment–age variables to identify critical changes in forest structure and dynamics. This understanding is essential for maintaining forest productivity, structural and species diversity, stability, and sustainability. This study focused on measuring, reporting, and modeling these relationships for Araucaria angustifolia (Bertol.) Kuntze, across various diameters and three stands, located at different rural properties in southern Brazil. A random sample of 186 individual trees was acquired; the trees were measured for multiple dendrometric variables, and several morphometric indices were calculated. Additionally, two cores were extracted from each tree using an increment borer, enabling the measurement of growth rings and annual diameter increments. These were modeled using generalized linear models to assess the relationships among them and to quantify changes in forest structure and dynamics. The results revealed the dominance of A. angustifolia and a decline in the increment rate with increasing age, shape, and size in both old and young trees, indicating potential risks to the structure and dynamics of these unmanaged forests. Therefore, the models constructed in this study can guide conservation-by-use efforts and ensure the long-term continuity and productivity of forest remnants at selected rural properties, where A. angustifolia trees are predominant. Full article

Understanding how elevation gradients and soil depths influence soil organic carbon stocks (SOCS) and total nitrogen stocks (TNS) is essential for sustainable forest management (SFM) and climate change mitigation. This study investigated the effects of elevation and soil depth on SOCS and TNS in the Mount Kenya East Forest (MKEF). A stratified systematic sampling approach was applied, involving collection of 38 soil samples from two depths (0–20 cm and 20–40 cm) across three elevation zones: Lower Forest (1700–2000 m), Middle Forest (2000–2350 m), and Upper Forest (2350–2650 m). Samples were analysed for bulk density (BD), pH, texture, soil organic carbon (SOC), and total nitrogen (TN), using standard laboratory methods. In topsoil (0–20 cm), SOCS ranged from 109.28 ± 23.41 to 151.27 ± 17.61 Mg C ha⁻¹, while TNS varied from 8.89 ± 1.77 to 12.00 ± 2.46 Mg N ha⁻¹. In subsoil (20–40 cm), SOCS ranged from 72.03 ± 19.90 to 132.23 ± 11.80 Mg C ha⁻¹, with TNS varying between 5.71 ± 1.63 and 10.50 ± 1.90 Mg N ha⁻¹. SOCS and TNS increased significantly with elevation (p < 0.05), exhibiting the following trend: Lower Forest < Middle Forest < Upper Forest. Topsoil consistently stored significantly higher SOCS than subsoil (p < 0.05), emphasizing the critical role of surface soils in carbon sequestration. Regression analysis revealed a significant positive relationship between SOCS and TNS (R² = 0.84, p < 0.001). Both SOCS and TNS were positively correlated with elevation, SOC, TN, and total annual precipitation (TAP), but negatively correlated with BD and mean annual temperature (MAT). These findings provide baseline data for monitoring SOCS and TNS in the MKEF, offering insights into sustainable forest management strategies to improve soil health and enhance climate change mitigation efforts. Full article

Plants serve as the basis for ecosystems and provide a wide range of essential ecological, environmental, and economic benefits. However, forest plants and other forest systems are constantly threatened by degradation and extinction, mainly due to misuse and exhaustion. Therefore, sustainable forest management (SFM) is paramount, especially in the wake of global climate change and other challenges. SFM ensures the continued provision of plants and forests to both the present and future generations. In practice, SFM faces challenges in balancing the use and conservation of forests. This review discusses the transformative potential of artificial intelligence (AI), machine learning, and deep learning (DL) technologies in sustainable forest management. It summarizes current research and technological improvements implemented in sustainable forest management using AI, discussing their applications, such as predictive analytics and modeling techniques that enable accurate forecasting of forest dynamics in carbon sequestration, species distribution, and ecosystem conditions. Additionally, it explores how AI-powered decision support systems facilitate forest adaptive management strategies by integrating real-time data in the form of images or videos. The review manuscript also highlights limitations incurred by AI, ML, and DL in combating challenges in sustainable forest management, providing acceptable solutions to these problems. It concludes by providing future perspectives and the immense potential of AI, ML, and DL in modernizing SFM. Nonetheless, a great deal of research has already shed much light on this topic, this review bridges the knowledge gap. Full article

Most experiments on thinning effects are based upon short-term outcomes, which has led to a common conclusion that stand gross volume will be reduced after various thinning operations. However, contrary results are emerging from more recent long-term thinning experiments. The well-known biological concept of compensatory growth was introduced to reconcile these opposing results. This synthetic article describes a systematic investigation on overcompensation under the conceptual framework of compensatory growth and consists of the following: (1) empirical evidence of overcompensation in forests; (2) a theoretical proof of the possibility of emerging overcompensation using a life-history-theory based analytical tree adaptive growth (TAG) model; and (3) an empirical data-based tree compensatory growth (TreeCG) model that resembles the growth relationships from natural stands. Our results indicate that (1) overcompensation is an expected common phenomenon across different tree species and geographical regions, and (2) overcompensation can be predicted from at least two different mechanisms: optimal allocation of available energy to growth, reproduction, maintenance and reserves, and redistribution of freed resources from dead trees. Therefore, overcompensation is a predictable phenomenon, and forest managers can make SFM (Sustainable Forest Management) decisions based on their specific management goals. Research recommendations are suggested for next steps. Full article

Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory—a critical area for assessing logging impacts—remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rondônia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts. Full article

Ensuring the sustainability of forests is among the priority measures to be taken against the decline in biodiversity, which is among the world’s increasingly common concerns. This study investigated whether sustainable forest management processes are considering wildlife conservation objectives. Ten forest management processes were categorized and then analyzed for whether wildlife conservation is adequately considered. The wildlife data were grouped into four categories, with the most common being the protection of biodiversity and wildlife trade. The satisfaction level obtained according to the scoring method used was determined as the criterion of success in wildlife conservation. According to the scoring method applied, the overall success was found to be 50%. It was determined that a standard should be developed regarding the economic value of wildlife fauna and flora species and that this issue should be included in sustainable forest management strategies. Only 20 of 116 total sustainable forest management criteria considered wildlife. The African Timber Organization process, which has the most member countries, was identified as the process with the lowest number of wildlife criteria, at 2%, while the International Tropical Timber Organization process was found to have the most wildlife protection criteria at 20%. The conservation success rates for the two processes of which Türkiye is a member were also found to be quite low. It is concluded that there is a need to strengthen the place of wildlife, one of the most important living components for forests, in SFM processes both for Türkiye and internationally. The results obtained were evaluated both in terms of international criteria and practices in Türkiye. It is also recommended that future international meetings include wildlife health and diversity as a separate criterion when determining sustainable methods. Full article

Forest degradation and forest disturbance are distinct yet often conflated concepts, complicating their definition and monitoring. Forest degradation involves interrupted succession and a severe reduction in forest services over time, caused by factors like fires, illegal selective logging, and edge effects. Forest disturbance, on the other hand, refers to abrupt, localized events, natural or anthropogenic, such as legal selective logging, tropical blowdowns, storms, or fires, without necessarily leading to long-term degradation. Despite the varying intensity and scale of forest degradation and disturbance, systematic studies distinguishing its types and classes are limited. This study reviews anthropogenic impacts on forests in the Brazilian Amazon, analyzing 80 scientific articles using remote sensing techniques and data. Most research focuses on the “arc of deforestation,” characterized by intense human activity, showcasing methodological advancements but also revealing gaps in monitoring less-studied regions like the central and western Amazon. The findings emphasize the need for advanced remote sensing tools to differentiate degradation types, particularly in sustainable forest management (SFM) contexts. Expanding research to underrepresented regions and refining methodologies are crucial for better understanding forest dynamics and improving conservation strategies. These efforts are essential to support effective forest management and informed policy development across the Amazon. Full article

The three-dimensional reconstruction of forests is crucial in remote sensing technology, ecological monitoring, and forestry management, as it yields precise forest structure and tree parameters, providing essential data support for forest resource management, evaluation, and sustainable development. Nevertheless, forest 3D reconstruction now encounters obstacles including higher equipment costs, reduced data collection efficiency, and complex data processing. This work introduces a unique deep learning model, CPH-Fmnet, designed to enhance the accuracy and efficiency of 3D reconstruction in intricate forest environments. CPH-Fmnet enhances the FPN Encoder-Decoder Architecture by meticulously incorporating the Channel Attention Mechanism (CA), Path Aggregation Module (PA), and High-Level Feature Selection Module (HFS), alongside the integration of the pre-trained Vision Transformer (ViT), thereby significantly improving the model’s global feature extraction and local detail reconstruction abilities. We selected three representative sample plots in Haidian District, Beijing, China, as the study area and took forest stand sequence photos with an iPhone for the research. Comparative experiments with the conventional SfM + MVS and MVSFormer models, along with comprehensive parameter extraction and ablation studies, substantiated the enhanced efficacy of the proposed CPH-Fmnet model in addressing difficult circumstances such as intricate occlusions, poorly textured areas, and variations in lighting. The test results show that the model does better on a number of evaluation criteria. It has an RMSE of 1.353, an MAE of only 5.1%, an r value of 1.190, and a forest reconstruction rate of 100%, all of which are better than current methods. Furthermore, the model produced a more compact and precise 3D point cloud while accurately determining the properties of the forest trees. The findings indicate that CPH-Fmnet offers an innovative approach for forest resource management and ecological monitoring, characterized by cheap cost, high accuracy, and high efficiency. Full article

Sustainable Forest Management (SFM) can fully use forest resources and improve the economic, environmental, and social sustainability of forest areas. Forestry enterprises play a crucial role in the implementation of SFM. However, the previous literature on SFM pays little attention to the subject of forestry enterprises. This paper aims to extend research on SFM from a macro perspective to the micro level of forestry enterprises. Taking the Triple Bottom Line (TBL) as a theoretical framework and the Montreal Process Criteria and Indicators (MP C&Is) as a basis, this paper constructs an indicator system to evaluate the performance of SFM of forestry enterprises from economic, social, and environmental aspects. Then, we apply the hybrid Multi-Criteria Decision-Making (MCDM) methods, i.e., the Best–Worst Method (BWM) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, to construct the methodological system for SFM performance evaluation of forestry enterprises. Through a questionnaire survey of 68 academics and researchers, we determine the weights of the SFM indicators and select a representative forestry enterprise as a case study. The effectiveness of this SFM performance evaluation model is then demonstrated through its application to the case study of forestry enterprises in China. Through the application of the model, this paper evaluates the enterprise’s SFM performance over the five-year period 2017–2021 and proposes appropriate policy recommendations and improvements. It is found that environmental factors are the primary factors of SFM in forestry enterprises. Forestry enterprises should not only pay attention to economic benefits but also to the use of forest resources and the protection of forest ecosystems to better achieve SFM. Full article

Accurate forest parameters are crucial for ecological protection, forest resource management and sustainable development. The rapid development of remote sensing can retrieve parameters such as the leaf area index, cluster index, diameter at breast height (DBH) and tree height at different scales (e.g., plots and stands). Although some LiDAR satellites such as GEDI and ICESAT-2 can measure the average tree height in a certain area, there is still a lack of effective means for obtaining individual tree parameters using high-resolution satellite data, especially DBH. The objective of this study is to explore the capability of 2D image-based features (texture and spectrum) in estimating the DBH of individual tree. Firstly, we acquired unmanned aerial vehicle (UAV) LiDAR point cloud data and UAV RGB imagery, from which digital aerial photography (DAP) point cloud data were generated using the structure-from-motion (SfM) method. Next, we performed individual tree segmentation and extracted the individual tree crown boundaries using the DAP and LiDAR point cloud data, respectively. Subsequently, the eight 2D image-based textural and spectral metrics and 3D point-cloud-based metrics (tree height and crown diameters) were extracted from the tree crown boundaries of each tree. Then, the correlation coefficients between each metric and the reference DBH were calculated. Finally, the capabilities of these metrics and different models, including multiple linear regression (MLR), random forest (RF) and support vector machine (SVM), in the DBH estimation were quantitatively evaluated and compared. The results showed that: (1) The 2D image-based textural metrics had the strongest correlation with the DBH. Among them, the highest correlation coefficient of −0.582 was observed between dissimilarity, variance and DBH. When using textural metrics alone, the estimated DBH accuracy was the highest, with a RMSE of only 0.032 and RMSE% of 16.879% using the MLR model; (2) Simply feeding multi-features, such as textural, spectral and structural metrics, into the machine learning models could not have led to optimal results in individual tree DBH estimations; on the contrary, it could even reduce the accuracy. In general, this study indicated that the 2D image-based textural metrics have great potential in individual tree DBH estimations, which could help improve the capability to efficiently and meticulously monitor and manage forests on a large scale. Full article

Precision forestry is a useful technique to help forest stakeholders with proper sustainable forest management. Modern sensors and technologies, with special reference to the sustainability of forest operations, can be applied on a variety of levels, including the monitoring of forest activities regarding the three pillars (economy, environment, and society). In this review, we summarised the current level of knowledge regarding the use of precision forestry techniques for monitoring forest operations. We concentrated on recent data from the last five years (2019–2023). We demonstrated how an Industry 4.0 strategy for remote and proximal monitoring of working performance can be effective when using CAN-bus and StanForD data collected by modern forest machines. The same information can be effectively used to create maps of soil trafficability and to evaluate the patterns of skid tracks or strip roads built as a result of forest intervention. Similar information can be gathered in the case of small-scale forestry by using GNSS-RF (Global Navigation Satellite Systems—Radio Frequency) or even monitoring systems based on smartwatches or smartphones. LiDAR and Structure for Motion (SfM) photogrammetry are both useful tools for tracking soil rutting and disturbances caused by the passage of forest machinery. SfM offers denser point clouds and a more approachable method, whereas laser scanning can be considerably faster but needs a more experienced operator and better data-processing skills. Finally, in terms of the social component of sustainability, the use of location sharing technologies is strongly advised, based for instance on GNSS—RF to monitor the security of forest workers as they operate. Full article

Multilevel governance (MLG) and co-management are indispensable to sustainable forest management (SFM) and the maintenance of ecosystem services (ES). Through interviews with key external and community actors and workshops in two ejidos in central Mexico, we qualitatively analyzed information on the historical and current uses of forests and the ways the ejidos evolved toward SFM by implementing MLG regulatory frameworks that enabled forest co-management. By studying the perceptions of local stakeholders and the relevance of their interactions to effective local forest management, we found that the history of forest use in the region has favored the local appropriation of socioecosystems through co-management. However, these principles have not simply led to harmonized development and conservation. Although such technical interventions favor forest productivity in local communities, the stakeholders also recognize that certain adjustments could improve and maintain the local ES. Thus, while the conditions of community forest management in Mexico exemplify how local decision-making processes can be both relatively democratic and ecologically beneficial for local communities, it is also necessary to move from a traditional management model to shared governance in community contexts. Full article

Indonesia is the largest archipelagic country in the world, with 17,000 islands of varying sizes and elevations, from lowlands to very high mountains, stretching more than 5000 km eastward from Sabang in Aceh to Merauke in Papua. Although occupying only 1.3% of the world’s land area, Indonesia possesses the third-largest rainforest and the second-highest level of biodiversity, with very high species diversity and endemism. However, during the last two decades, Indonesia has been known as a country with a high level of deforestation, a producer of smoke from burning forests and land, and a producer of carbon emissions. The aim of this paper is to review the environmental history and the long process of Indonesian forest management towards achieving environmental sustainability and community welfare. To do this, we analyze the milestones of Indonesian forest management history, present and future challenges, and provide strategic recommendations toward a viable Sustainable Forest Management (SFM) system. Our review showed that the history of forestry management in Indonesia has evolved through a long process, especially related to contestation over the control of natural resources and supporting policies and regulations. During the process, many efforts have been applied to reduce the deforestation rate, such as a moratorium on permitting primary natural forest and peat land, land rehabilitation and soil conservation, environmental protection, and other significant regulations. Therefore, these efforts should be maintained and improved continuously in the future due to their significant positive impacts on a variety of forest areas toward the achievement of viable SFM. Finally, we conclude that the Indonesian government has struggled to formulate sustainable forest management policies that balance economic, ecological, and social needs, among others, through developing and implementing social forestry instruments, developing and implementing human resource capacity, increasing community literacy, strengthening forest governance by eliminating ambiguity and overlapping regulations, simplification of bureaucracy, revitalization of traditional wisdom, and fair law enforcement. Full article

The 2030 global agenda for sustainable development integrates social, economic, and environmental dimensions, emphasizing peace, human rights, gender equality, and women’s empowerment. The SDG framework, consisting of 17 goals, 169 targets (SDTs), and 231 indicators (SDIs), forms a complex, interconnected network that necessitates extensive research. Despite prior studies on SDG interlinkages, the integration of Climate Change (CC), Sustainable Forest Management (SFM), and Information and Communication Technology (ICT), collectively known as CSI Nexus, remains underexplored. This study addresses this gap by identifying SDTs aligned with CC, SFM, and ICT (CSI) and analyzing their linkages within the SDG framework using a systems thinking approach. The objectives are to (1) investigate and identify SDTs connected with the CSI Nexus and (2) assess the significant relationship between and among CC, SFM, and ICT. The primary method involves a simplified meta-analysis and systems thinking approach incorporating content analysis, network visualization, affiliation matrix mapping, frequency distributions, and Spearman’s rho correlation. Results reveal 56 SDTs directly connected within CC + SFM + ICT, 16 within CC + SFM, one within SFM + ICT, and 51 within ICT + CC. The analysis indicates CC is significantly associated with SFM, while ICT has no significant association with CC and SFM, asserting minimal influence of ICT and SFM on the SDG 2030 framework. This research provides significant insights for decision-makers and stakeholders, contributing as a science-informed guide for priority-setting, policy coherence, and decision-making supporting the 2030 Sustainable Development Goals across sectors. Full article

This paper investigates issues confronting forest management and sustainability, focusing on the governance of the community forest user group (CFUG) initiative in Nepal. The paper begins with a literature review to give a general overview of the historical and current situation of forest governance in Nepal. It explores the historical impacts of unsustainable logging in Nepal and the World Bank Report, which both investigated and explored avenues for improving the forest situation, including community forestry. The paper outlines the development of community forestry, the legislative, regulatory, and governance frameworks underpinning this unique system of community-driven forest management, and its relationship to sustainable forest management (SFM). SFM in turn has engendered a market for sustainably derived timber and labeling systems for ‘good’ wood. The paper continues by providing an analysis of stakeholder attitudes regarding the current forest governance situation in Nepal. Furthermore, it provides another small case study on how such standards might be applied in the local community context of protecting Nepal’s Red Panda while simultaneously delivering sustainable forest management and community development. It concludes with a discussion on the need for governance standards for forest management and community forestry in Nepal. Full article