Management Of Forest Resources Research Articles

Estimating Forest Stock Volume Based on Airborne Lidar Data

Abstract. Forest stock volume (FSV) stands as an important indicator in evaluating the potential for carbon sequestration. It is crucial for forest resource management at local, regional, and national scales. In order to achieve an accurate estimation of FSV, this article takes Mengyin County, Shandong Province, China as the research area, builds a random forest (RF) model for four tree species based on airborne Lidar data, and forms a monitoring system of "individual tree - grid - county" granularities. The results demonstrated that all four models exhibited excellent generalization capabilities, with no signs of overfitting. In the test phase, the R2 of the poplar and pine models exceeded 0.9, while the R2 of the cypress model was 0.81, and the rRMSE was controlled within 20%, indicating that the fitting effect of the three tree species models was better; the accuracy of the robinia pseudoacacia model was relatively poor, with R2 of 0.60 and rRMSE of 20.60%. This study provides a feasible method for estimating forest stock volume within the county, which provides strong technical support for forest resource management and planning, and helps promote sustainable forestry development.

Effects of Environment Change Scenarios on the Potential Geographical Distribution of Cunninghamia lanceolata (Lamb.) Hook. in China

Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will help to adjust the planting range reasonably according to the habits and climate change, thus contributing to its survival and growth. Based on the MaxEnt model and ArcGIS tool, climate, soil, terrain, human activities, variable environment layers, and 395 C. lanceolata distribution points were used to simulate and analyze the geographical distribution characteristics of C. lanceolata in the current and future periods (the 2050s and 2070s) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The results showed that C. lanceolata was suitable to grow in a subtropical monsoon climate with warm, humid, abundant rainfall and a relatively gentle topography. Additionally, using percent contribution, permutation importance, and the knife-cutting test, we noted that the annual precipitation (Bio12), human activities (Hfp), minimum temperature of the coldest month (Bio6), mean temperature of the coldest quarter (Bio11), precipitation of coldest quarter (Bio19), annual temperature range (Bio7), and elevation were the leading environmental factors affecting the geographical distribution of C. lanceolata. Among them, it should be noted that the impact of human activities was negatively correlated with suitable habitat areas of C. lanceolata and led to the degeneration of suitable habitats and fragmentized distribution. In addition, predictions have shown that the areas of habitats under other scenarios will be characterized by an increasing and then decreasing trend by the 2050s and 2070s, except for the RCP2.6 scenario, under which the suitable habitats area of C. lanceolata will increase continuously. The core distributional shifts showed that the suitable habitats of C. lanceolata will gradually shift and migrate to high-latitude areas due to global warming. This study focused on the characteristics of suitable habitats of C. lanceolata under different climatic scenarios using more environmental factors and scenarios than before, aiming to provide a theoretical basis and guidance for the management and utilization of forest resources, the planning of suitable planting areas, and germplasm protection.

Effect of artisanal gold mining on woody plant diversity in Western Burkina Faso

Although artisanal gold mining strongly contribute to enhance people incomes in West Africa, this activity can lead to habitat fragmentation and loss of woody species. Therefore, studying the impact of artisanal gold mining on woody plant diversity is needed for efficient management of forest resources. In this study, we investigated the impact of artisanal gold mining on woody plant diversity and dynamics in the Kari classified forest in Western Burkina Faso. Forest inventories were carried out at two sites including a gold panning site and a control site, following a systematic sampling design. We identified all woody species and measured all woody species with a diamtere at breast height ≥5 cm. A total 60 plots of 900 m² were surveyed. Diversity indices and dendrometric parameters were calculated and compared between the two sites. The results showed a higher species richness (59 woody species distributing in 39 genera and 18 families) in the control site compared to the gold panning site (38 species distributing in 25 genera and 12 families). The mean values of the number of species per plot and the Shannon diversity index were higher in the control site (15.50 ± 8.80; 2.16 ± 0.43, respectively) than in the artisal mining site (5.47 ± 2.71; 1.40 ± 0.53, respectively), showing that the control site is more diverse than the site that is under gold panning. The control site also had the highest density (683.33 ± 262.99 ft/ha) and stable woody stands compared to the less gold panning site (137.41 ± 102.31 ft/ha) where unstable woody stands were found. While the gold-panning site was characterised by biophysical impacts such as destruction of the plant cover and soil excavation, no visible impact was observed at the control site.Theses findings highlight the negative impact of artisanl gold mining on woody species diversity and structure. The study provides essential information for decision-making on sustainable forest management in the context of mining boom in Burkina Faso's.

Kajian Alternatif Proyeksi Jaringan Jalan Hutan untuk Perencanaan Pemanenan Kayu di PT Inhutani I Labanan, Propinsi Jawa Barat

Forests are one of the natural resources that can be utilized in the form of both wood and non-wood forest products. Harvesting carried out to extract wood results in negative impacts such as soil damage and damage to standing stands. The negative impacts of wood harvesting can be reduced by planning in advance, such as creating a forest road network. The research was carried out in the mahogany room, utilization division, forest management department, while the assessment of the forest road network used the case study of PT Inhutani I Lebanan. The aim of the research is to examine the planning and selection of the best alternative forest road network for harvesting wood using multi-criteria decision making techniques. The research will be carried out in March 2023. The research stage begins with sketching the road network; assess road networks using economic, ecological and technical criteria; as well as conducting literature studies from various sources. The research results show that there are six alternative forest road networks for harvesting wood. This is based on forest road network assessment parameters that the best alternative road network in the PT Inhutani I Labanan road scheme is the 6th road network alternative. Sustainable management of forest resources can be achieved through a well-planned road network. The highest total utility value (rank 1) is found on the 6th road network which is the best alternative forest road network to be implemented at PT Inhutani Labanan I both in terms of technical aspects, ecological aspects and economic aspects.

ASSESSMENT OF FOREST DISTURBANCES USING REMOTE SENSING: CASE OF KALIWA RIVER FOREST RESERVE (KRFR), PHILIPPINES

Abstract. The Kaliwa River Forest Reserve (KRFR) is located in the southwestern part of the Sierra Madre Mountain Range, Philippines. KRFR is one of the most important forest reserves in the country that is adjacent to urban areas and is very much affected by various forest disturbances. Forest disturbances can be caused by either anthropogenic or natural phenomena (e.g., typhoons, drought, and pathogen outbreaks) and may have negative effects on the forest structure and function. The study used the Normalized Difference Moisture Index (NDMI) in detecting forest disturbances for the period of 2010, 2015, and 2020. Results showed that the deforestation rate has significantly decreased from 6.22% in 2010 to 3.40% in 2020. The National Greening Program (NGP) and intensified forest protection activities of the Department of Environment and Natural Resources (DENR) may have contributed to the observed decreasing trend in deforestation rate. However, forest disturbance in the closed and open forest areas of KRFR gradually increased from 0.11% to 0.40%. Based on the accuracy assessment, the NDMI performed well in determining forest disturbances with mean values of 0.91 and 0.93 for completeness and correctness, respectively. The main contributor to forest disturbances in KRFR was mainly anthropogenic factors such as kaingin, illegal logging, urban sprawl, and upland farming. Outputs of this study can aid in the formulation of policies for better forest resource management, rehabilitation, and protection in the protected area.

Normalized Difference Vegetation Index (NDVI) marked reliable value of Ulu Muda Forest Reserve

Water resources must be preserved because they are vital for agricultural, industrial, and domestic use, especially in tropical forest regions. Given the importance of the Ulu Muda Forest Reserve to the Kedah state in sustaining paddy cultivation for the country, this study aimed to provide reliable forest classification method and produce Normalized Difference Vegetation Index-Maximum Likelihood Classification of forest density map for Ulu Muda Forest Reserve. Here, the study reports measurements of the satellite remote sensing index for the forest based the image date. The forest recorded NDVI index of 0.72 to 0.98 for high density, NDVI index of 0.61 to 0.71 for moderate and NDVI index of 0.26 to 0.60 for low density area. The index showed higher value, which is comparable with reports of other tropical forest elsewhere. The results were influenced using satellite remote sensing pre-processing, processing, and classification. As a result, based on the satellite image indices approach, upper threshold of the higher density, the forest can exhibit higher capability for sustaining its ecological and hydrological functions. Given that most of the tropical forest biome has sustained this threshold, forest management should be sustained at this state, and more measures should be taken for example increase of protection forest classification in Rancangan Pengurusan Hutan (RPH) that is one of yearly planning report in states of Peninsular Malaysia. In the meantime, Geographical Information System (GIS) capacity should be mandate looking forwards for more utilisation in mapping for planning and projection of forest resource management activities.

Contributions of Community Forestry in Rural Social Transformation: Some Observations From Nepal

This paper examines the contribution of Nepalese Community Forestry in social change process in two central hills districts of Nepal. Analysis of data indicates improvement in forest management, decision making, representation of poor and women in forest management and utilisation, awareness on economic and ecological contributions of forests. Debates and discussionshave increased as regards roles and responsibilities of users as well as mechanisms for gender-sensitive, poverty-focused and equity-based forest management activities. All these factors are contributing to achieve sustainable management of forest resources, to improve the living condition of people and to promote non-coercive social transformation.

A Fast Detection Algorithm for Change Detection in National Forestland “One Map” Based on NLNE Quad-Tree

The National Forestland “One Map” applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in “One Map”, taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for “One Map” (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in “One Map” and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data.

The role of internal culture for coping with uncertainty in forest management

Theories about uncertainty usually focus on its reduction. However, a significant source of uncertainty lies in the innovative and creative capabilities of the human mind. This particular source entangles us in contradictions: both reducing and creating uncertainty simultaneously. Therefore, a theory of uncertainty that solely investigates ways to reduce it is incomplete. It must be complemented by aspects of how uncertainty contributes the betterment of people.In our study, we establish a foundation for extending the theory of uncertainty in five steps. Drawing on the insights of William A. Duerr and Jean B. Duerr, who assert that faith, rather than measureable facts, guides forest resource management under uncertainty, we concentrate our analysis on informal institutions, customs, traditions etc.Firstly, we reconstruct the evolution of the internal culture of forestry. Secondly, we delineate the bourgeois culture and compare it with the internal culture of forestry. Thirdly, we develop a theoretical approach to the re-valuation of uncertainty. Fourthly, we combine the theoretical approach with a historical analysis of the Great Enrichment to provide a comprehensive understanding of an overall picture how people re-valuated uncertainty. This involves placing higher value on the chances of self-induced unforeseen changes higher than on the associated dangers. Finally, we summarize our findings and close the paper with suggestions for future research on the extension of a theory of uncertainty.

Suitability assessment for forest landscape restoration based on species diversity conservation

Forest landscape fragmentation poses a significant challenge to the conservation of species diversity. Evaluating the suitability of forest landscape restoration (FLR) becomes essential as it promotes effective management of forest resources and protects species diversity. This study focused on deploying and testing a suitability evaluation framework system for FLR in the Saihanba area of Hebei, China. The framework system incorporated factors such as land cover type, landscape structure, soil quality, and topography. Using the matter-element model, the study evaluated the restoration suitability and identified priority restoration areas. The results revealed that land cover type and landscape structure were the primary factors influencing FLR in the study area. The suitable area for FLR in the study area was about 519.20 km2, accounting for 55.55% of the total area. The area of moderate, marginal, and unsuitable restoration accounted for 17.30%, 23.96%, and 3.18% of the study area, respectively. The suitable restoration areas predominantly consisted of natural secondary forest characterized by large patch areas, short distances between patches, and complex patch shapes. In the study area, the objective of FLR should be protect large and complex shaped patches of natural secondary forests for better conservation of species diversity. The FLR suitability evaluation conducted in this study provide a valuable guidance for promoting species-diversity conservation and restoration of forest ecosystems at the landscape scale in the study area and other similar regions.

The Role of Remote Sensing in Forest Cover Changes in Butambala District, Central Uganda

Natural forests in Uganda have experienced changes which must be quantified to inform future forest resource management and avoid the danger of reduced ecosystem service benefits. The primary purpose of this study was to assess forest changes in Butambala District by employing remote sensing techniques and GIS tehniques. The original contribution is the capacity to detect Central Forest Reserve cover decline using remote sensing in the study area for a 27-year period (1995-2020). Landsat 5 and Sentinel 2 images were processed using a supervised image classification approach to identify and quantify land use/cover changes. The study results indicated that the district has undergone land cover/use changes over the last 27 years. The prevailing changes in the district from 1995 to 2022 were expansions of built-up areas from (0.54% to 4.22%), agriculture from (42.38% to 79%), and decreases in grassland from (29.9% to 0%), natural forest cover from (14.07% to 5.9%), wetland from (8.24% to 7.9%), tree plantation from (4.87% to 3.3%). The changes threaten the environment and quality of life of people. Therefore, there is need to take critical and practical measures to regulate land use and landcover changes and conserve natural resources in Butambala District

Actors participation and power relations of REDD+ implementation in Bale Eco Region, Ethiopia

ABSTRACT The study focuses on actor participation and power dynamics in the REDD+ project within the Bale eco-region, a vital facet of global climate policy for sustainable forest resource management. The Forest Carbon Partnership Facility (FCPF) of the World Bank supports developing countries in preparing for international REDD+ policy implementation, emphasizing the necessity of effective actor participation. The study uses text documents, Socio metric survey, and interviews with project actors and participants. This study employs both qualitative framework and social network analysis (SNA). While SNA looks at power dynamics and interactions between actors, the qualitative framework allows for a comprehensive evaluation of actor participation. The social network data was analyzed using an open source software called NodeXL. Results show that Bale eco-region REDD+ involves actors from different groups. Actors contribute to national strategies, funding, capacity building, and knowledge sharing. However, local communities have limited involvement in decision-making, lack knowledge sharing and capacity building. This hinders the implementation process and contributes to unequal power dynamics and lack the comprehensive input needed for effective and tailored strategies in the region. Calculated centrality values highlight a strong alliance among Ethiopia's national government entity overseeing REDD+ Environment, Forest and Climate Change Commission (EFCCC), and the regional entities Oromiya Forest and Wildlife Enterprise (OFWI), as well as Farm Africa, indicating significant institutional power across national and local levels. EFCCC holds influence in decision-making and strategy development, while OFWI wields substantial regional institutional power in information control and collaboration. Thus, climate policies must prioritize involvement of local communities in decision-making, capacity development, and resource accessibility. This helps to tackle power imbalances through collaborative governance structures. It also enables stakeholders to actively manage forests through imparting skills, knowledge and share benefits equally.

Harmonised statistics and maps of forest biomass and increment in Europe

Forest biomass is an essential resource in relation to the green transition and its assessment is key for the sustainable management of forest resources. Here, we present a forest biomass dataset for Europe based on the best available inventory and satellite data, with a higher level of harmonisation and spatial resolution than other existing data. This database provides statistics and maps of the forest area, biomass stock and their share available for wood supply in the year 2020, and statistics on gross and net volume increment in 2010–2020, for 38 European countries. The statistics of most countries are available at a sub-national scale and are derived from National Forest Inventory data, harmonised using common reference definitions and estimation methodology, and updated to a common year using a modelling approach. For those counties without harmonised statistics, data were derived from the State of Europe’s Forest 2020 Report at the national scale. The maps are coherent with the statistics and depict the spatial distribution of the forest variables at 100 m resolution.

Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age

Accurate classification of forest tree species holds great significance in the context of forest biodiversity assessment and the management of forest resources. In this study, we utilized Sentinel-2 time series data with high temporal and spatial resolution for tree species classification. To address potential classification errors stemming from spectral differences due to tree age variations, we implemented the Continuous Change Detection and Classification (CCDC) algorithm to estimate tree ages, which were integrated as additional features into our classification models. Four different combinations of classification features were created for both the random forest (RF) algorithm and extreme gradient boosting (XGB) algorithm: spectral band (Spec), spectral band combined with tree age feature (SpecAge), spectral band combined with spectral index (SpecVI), and spectral band combined with spectral index and tree age feature (SpecVIAge). The results demonstrated that the XGB-based models outperformed the RF-based ones, with the SpecVIAge model achieving the highest accuracy at 78.8%. The incorporation of tree age as a classification feature led to an improvement in accuracy by 2% to 3%. The improvement effect on classification accuracy varies across tree species, due to the varying uniformity of tree age among different tree species. These results also showed it is feasible to accurately map regional tree species based on a time-series multi-feature tree species classification model which takes into account tree age.

Analysis of actors and power relationships in forest encroachment study case: Ledang Village KPH Ropang

Deforestation caused by the stakeholders who are trying to revitalize and reposition their role in forest resource management. Research related to actors was carried out to find who had an interest and influence in that problem, what were the factors of forest encroachment and how was the power relationship between stakeholders involved in the problem of forest encroachment. The research located in Ledang villages, RPH Temutung with research sampling technique was clustered purposive sampling. Determination of respondents using key informants. Data analysis; (1) stakeholders mapping analysis, (2) forest encroachment analysis (3) power relations analysis. The results showed that the stakeholders involved were mapped into three quadrants grouped: (1) key player: forest community group, community, ropang forest management unit, temutung forest management resort, (2) subject: hodge and sawmills, (3) context setters: village government, district government, community figure, village traditional institutions and civil servant. The factors were economic, social, regulation, politic, and cultural factors. Stakeholder who have the big influence were community, and village government.

Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.

Climate sensitive growth and yield models in Canadian forestry: Challenges and opportunities

Growth and yield models in forest management planning are used to project future forest conditions and estimate quantities such as wood volume and biomass. These models are crucial for assessing forest sustainability, however, some models currently used in Canada do not adequately account for climate and other environmental variables, which limits their effectiveness under a changing climate. Climate-sensitive growth and yield models (CSGYMs) are therefore urgently needed to support forest management decisions. The Canadian Forest Service (CFS) has developed a strategic plan to advance climate-sensitive growth and yield modeling in Canada through collaboration with provincial and territorial agencies, as well as other partners. The primary objective of this plan is to provide a national-level modelling approach to predicting and managing forest growth, mortality, and other ecosystem services. The climate sensitive growth and yield modelling initiative emphasizes collaboration, open data, and open-source code principles to ensure widespread accessibility and uptake of models, thus contributing to the sustainable management of forest resources. This technical review reports on the status of growth and yield models currently applied in each province and territory, assesses the level of climate sensitivity associated with each of these models, synthesizes the relevant modeling approaches and input data required to implement climate sensitivity into these models, and suggests possible pathways for achieving CSGYM at a national scale. Widespread collaboration will be the key to advancing the development of CSGYMs.

Forest Canopy Height Retrieval and Analysis Using Random Forest Model with Multi-Source Remote Sensing Integration

Forest canopy height is an important indicator of the forest ecosystem, and an accurate assessment of forest canopy height on a large scale is of great significance for forest resource quantification and carbon sequestration. The retrieval of canopy height based on remote sensing provides a possibility for studying forest ecosystems. This study proposes a new method for estimating forest canopy height based on remote sensing. In this method, the GEDI satellite and ICESat-2 satellite, which are different types of space-borne lidar products, are used to cooperate with the Landsat 9 image and SRTM terrain data, respectively. Two forest canopy height-retrieval models based on multi-source remote sensing integration are obtained using a random forest regression (RFR) algorithm. The study, conducted at a forest site in the northeastern United States, synthesized various remote sensing data sets to produce a robust canopy height model. First, we extracted relative canopy height products, multispectral features, and topographic data from GEDI, ICESat-2, Landsat 9, and SRTM images, respectively. The importance of each variable was assessed, and the random forest algorithm was used to analyze each variable statistically. Then, the random forest regression algorithm was used to combine these variables and construct the forest canopy height model. Validation with airborne laser scanning (ALS) data shows that the GEDI and ICESat-2 models using a single data source achieve better accuracy than the Landsat 9 model. Notably, the combination of GEDI, Landsat 9, and SRTM data (R = 0.92, MAE = 1.91 m, RMSE = 2.78 m, and rRMSE = 12.64%) and a combination of ICESat-2, Landsat 9, and SRTM data (R = 0.89, MAE = 1.84 m, RMSE = 2.54 m, and rRMSE = 10.75%). Compared with the least accurate Landsat 9 model, R increased by 29.58%, 93.48%, MAE by 44.64%, 46.20%, RMSE by 42.80%, 49.40%, and the rRMSE was increased by 42.86% and 49.32%, respectively. These results fully evaluate and discuss the practical performance and benefits of multi-source data retrieval of forest canopy height by combining space-borne lidar data with Landsat 9 data, which is of great significance for understanding forest structure and dynamics. The study provides a reliable methodology for estimating forest canopy height and valuable insights into forest resource management and its contribution to global climate change.

Supervised terrestrial to airborne laser scanner model calibration for 3D individual-tree attribute mapping using deep neural networks

Capturing subcanopy forest information from airborne laser scanning (ALS) is constrained by signal occlusion. This study demonstrates the potential of close-range terrestrial laser scanner (TLS) scanning to mitigate the constraints of ALS in acquiring stem-level forest attributes. A transformer-based neural network was adapted to classify and segment 3D individual trees from ALS data. A deep neural network combined with a gaussian process layer was proposed to estimate tree diameter-at-breast-height (DBH) from ALS data. The performance of these methods was compared to other benchmarked methods using the same dataset, including a total of seven classifiers, five segmentors, and six attribute regressors. The study was conducted across four ALS sample areas and ten combined TLS/ALS plots, primarily in montane forests. Manual delineation of TLS trees provided a precise validation reference. The proposed methods demonstrated high accuracies, with a mean intersection-over-union (mIoU) of 0.92 for ALS tree classification, 0.70 for tree segmentation, and a RMSE of 4.2 cm or 18.9 % for DBH estimation on average of the ten plots. Tree detection accuracy was strongly associated with the final segmentation accuracy. Factors such as tree height, overlapping, inclination, and neighboring conditions impacted segmentation accuracy. Our segmentation method effectively mitigated accuracy loss for short and occluded trees. Overall, this study presents scalable and cost-effective solutions for TLS calibration of ALS scans over two meso-scale montane valleys. Leveraging deep neural networks enables scaling of stem attributes to landscape scales, thereby linking fine-scale forest inventory with sustainable management of expansive forest resources. Our codes are available at https://github.com/truebelief/artemis_treescaling.

Conceptualizing Forest Operations Planning and Management Using Principles of Functional Complex Systems Science to Increase the Forest’s Ability to Withstand Climate Change

The sustainable management of forest resources is greatly influenced by forest operations (FO). Interactions between humans and nature describe how people engage with and are impacted by the natural world. As we enter the Anthropocene epoch, we are being compelled to reevaluate our past and present methods of managing and planning our forest operations in order to find new ones that are more adaptable and successful at addressing the growing unpredictability resulting from accelerating global change. We briefly discuss the goals and constraints of the prior and current management and planning principles for forest operations in this study, focusing on how these principles have evolved on a worldwide scale. We then propose a promising idea, such as managing forest operations as complex adaptive systems and approaches based on resilience and sustainable use of forest resources, in order to achieve the necessary economic, social, and ecological goals. An in-depth understanding of the ecological, economic, and social factors that influence forest resilience is necessary for planning and managing forest operations efficiently. The proposed strategy combines the effectiveness of forest operations with a functional, complex network approach in order to manage forests for the Anthropocene.