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Log hauling costs are increased by the presence of closed and posted (restricted weight loading) bridges, as operators are forced to find alternative routes to wood-using mills. The USDA Forest Service Forest Inventory and Analysis (FIA) forest inventory, road network, bridge, and sawmill datasets were obtained for the US state of Mississippi. An 80.5 km buffer was established to define each sawmill's working area, which occasionally extended across state lines. Anetwork analysis derived 129 feasible shortest optimal trucking routes between 46 FIA plot locations and the sawmills. Thirty of these routes had either closed or posted bridges along the shortest path; only 13 viable alternative routes were identified due to distance and posted weight limits. The additional trucking distance for these workable alternative routes averaged 7.1 km. The affected routes and their legal, full payload alternatives were each fitted to atriangular distribution to better understand how costs were impacted by posted or closed bridges. If no regulated bridges were encountered, haul cost across arange of haul rates and distances ranged from $3.50 to $14.40 USD per tonne. Costs were from $3.90 to $16.00 USD per tonne along routes with posted or closed bridges. On average, the cost difference was $0.98 USD per tonne, which was an increase of 12.0%. These findings have implications for road infrastructure maintenance, log trucking, wood procurement, and ultimately the values of standing timber and timberland. [ABSTRACT FROM AUTHOR]

Tree‐growth stability is crucial in upholding forest ecosystem services. Despite extensive research on the correlation between tree growth and climate, the influence of forest understorey on tree‐growth stability remains understudied. We surveyed forest plots and collected tree‐ring samples along two elevational gradients, ranging from 3600 to 4400 m a.s.l., in the southeastern Tibetan Plateau. An index was developed to quantify individualistic growth stability of trees, and its linkage with the complexity of forest understorey structure was examined. We found that tree‐growth stability is more pronounced in complex forest communities. In forests with higher understorey complexity, the proportion of trees experiencing growth release increased during wet years, while those with growth suppression augmented during dry years. Interestingly, a subset of trees exhibits abnormal growth increases even during the driest years, whereas another subset shows abnormal growth reductions during the wettest years. Furthermore, forests with more complex understorey structures exhibited a higher proportion of trees showcasing these two types of ‘anti‐phase’ growth statuses. Synthesis. Our study underscores the linkage between individualistic growth stability of trees and the complexity of understorey structures. The growth stability of the overstorey layer is conveyed at the expense of individual‐level growth stability and synchrony in forests with complex understorey structures. These findings emphasize the necessity for attention to the interplay between tree growth and understorey community structures when assessing the impacts of future climate change on forest dynamics. [ABSTRACT FROM AUTHOR]

Global deforestation and forest degradation threaten the sustainability of natural and human systems. Forest landscape restoration, through active approaches such as plantations, woodlots, boundary planting, and agroforestry, and passive approaches like exclosures, presents an opportunity to mitigate adverse effects, enhance ecosystem service recovery, and associated benefits for livelihoods. Here, using different spatial scales, we compare the contribution of both approaches to the recovery of plant diversity in southern Ethiopia. Using forest inventory data (891 plots) from multi‐aged stands, we estimated and compared alpha (α), beta (β), and gamma (γ) diversity in regeneration and tree layers between the approaches. We observed increasing α‐diversity in the order grazing lands‐active‐passive‐forest sites. β‐Diversity revealed similarity between passively restored sites and natural forests. γ‐Diversity was higher in active restoration for the regeneration layer, but passive restoration had higher γ‐diversity in the tree layer. For both approaches, γ‐diversity was consistently highest in intermediate‐aged stands (10–20 years). Results highlight the potential of active restoration strategies to facilitate vegetation recovery in human‐dominated landscapes, especially when management allows natural regeneration, while stand age variation may be associated with disturbance intensities for both approaches. Our results support a paradigm shift toward implementation of a mixture of these approaches in the landscapes to meet increasing human demands while restoring important ecosystem services like biodiversity. We recommend enhancing species diversity on restored sites to improve performance and ecosystem service recovery. On actively restored sites, we recommend protecting regenerated species; on passively restored sites, enrichment planting, increased protection, and sustainable utilization. [ABSTRACT FROM AUTHOR]

LiDAR technology introduced the possibility of indirectly estimating various tree parameters. In 2020, Apple incorporated LiDAR sensors into their iPhone 12 Pro. This brings an opportunity to make data collection and plot scanning incredibly convenient. The main aim is to search for a tool and software to estimate the most accurate Diameter at Breast Height (DBH) for individual trees. Therefore, this study focuses on using an iPhone 12 Pro for data collection of individual trees. The performance of three software tools (ForestScanner, rTLS, RANSAC) for the estimation of DBH with field-estimated DBH was compared. The investigation of the significance of the estimation of DBH for each tree species was performed using the three algorithms. In this context, the scanning of 123 trees comprising six species: pine, oak, beech, hornbeam, spruce, and fir was done. DBH was estimated of the scanned tree point cloud using a built-in algorithm of ForestScanner application in the iPhone 12 Pro, RANSAC (CloudCompare plugin), and rTLS (an R-based package). In this study, ForestScanner showed the best results. Therefore, ForestScanner can be used as a reliable tool for measuring DBH. Inferences confirmed by ANOVA and Tukey post-hoc tests show that species significantly influence DBH. Accurate DBH estimation is crucial, and using lightweight devices like the iPhone can revolutionize the forest inventory sector in terms of the estimation of DBH. [ABSTRACT FROM AUTHOR]

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys. [ABSTRACT FROM AUTHOR]

Forests play a critical role in the provision of ecosystem services, and understanding their compositions, especially tree species, is essential for effective ecosystem management and conservation. However, identifying tree species is challenging and time-consuming. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have emerged as a promising technology for species identification due to their relatively low cost and high spatial and temporal resolutions. Moreover, the advancement of various deep learning models makes remote sensing based species identification more a reality. However, three questions remain to be answered: first, which of the state-of-the-art models performs best for this task; second, which is the optimal season for tree species classification in a temperate forest; and third, whether a model trained in one season can be effectively transferred to another season. To address these questions, we focus on tree species classification by using five state-of-the-art deep learning models on UAV-based RGB images, and we explored the model transferability between seasons. Utilizing UAV images taken in the summer and fall, we captured 8799 crown images of eight species. We trained five models using summer and fall images and compared their performance on the same dataset. All models achieved high performances in species classification, with the best performance on summer images, with an average F1-score was 0.96. For the fall images, Vision Transformer (ViT), EfficientNetB0, and YOLOv5 achieved F1-scores greater than 0.9, outperforming both ResNet18 and DenseNet. On average, across the two seasons, ViT achieved the best accuracy. This study demonstrates the capability of deep learning models in forest inventory, particularly for tree species classification. While the choice of certain models may not significantly affect performance when using summer images, the advanced models prove to be a better choice for fall images. Given the limited transferability from one season to another, further research is required to overcome the challenge associated with transferability across seasons. [ABSTRACT FROM AUTHOR]

Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R2 of 0.63 and an RMSE of 125.39 m3/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R2 of 0.73 and an RMSE of 128.86 m3/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R2 of 0.64 and an RMSE of 133.77 m3/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R2 of 0.70 and an RMSE of 21,120.72 m3/plot. These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT The data required for sustainable forest planning is provided by traditional forest inventories, which are labor, time, and cost‐intensive. Providing this data quickly, reliably, and accurately is crucial for planners and researchers. The objective of this study was to predict stand basal area (BA), stand volume (V), and quadratic mean diameter (dq) by leveraging vegetation indices (VIs) and reflectance (R) derived from Landsat 8 OLI and Sentinel 2 satellite images, along with topographic (T) data obtained from ALOS‐PALSAR satellite imagery. Forest inventory data for a total of 250 sample plots were used for modeling in the study. Stand parameters were estimated using support vector machines (SVM), multiple linear regression (MLR), decision tree (DT), and random forest (RF) algorithms. In modeling V, BA, and dq, both individual and combinations of R, VIs, and T values obtained from satellite imagery were used as independent variables. Using the generated datasets, each of the stand parameters was modeled separately with MLR, SVM, RF, and DT algorithms, and the success of the models was compared to determine the modeling technique and dataset with the highest success for the relevant parameter. The results showed that for each stand parameter, the highest model success was achieved in the combined dataset, which was created by combining all datasets. However, in terms of modeling techniques, the highest success for each stand parameter was achieved with different modeling techniques. The highest success for V is obtained in the model using the SVM method (R2 = 0.78; RMSE = 0.28 m3/ha), the RF method yielded the highest model performance for BA (R2 = 0.70; RMSE = 2.53 m2/ha), and finally, the highest success for dq was obtained in the DT method (R2 = 0.74; RMSE = 0.02 cm). In general, the datasets obtained from Sentinel 2 images showed higher model success than the datasets obtained from Landsat 8 OLI images. [ABSTRACT FROM AUTHOR]

The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, emphasizing their applications using LiDAR and imagery data. This review covers the challenges, progress, and application of these strategies in ecological monitoring, forest inventory, and tree species classification. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. This review highlights the effectiveness of semantic and instance segmentation techniques in forestry applications, such as precise tree species identification and individual tree monitoring. However, challenges such as occlusions, overlapping branches, and varying data quality remain. Future research should focus on overcoming these obstacles to enhance the precision and applicability of these segmentation methodologies. [ABSTRACT FROM AUTHOR]

Societal Impact Statement Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net‐zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi‐automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net‐zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well‐documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi‐automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground‐based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground‐based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories. [ABSTRACT FROM AUTHOR]

Key message: Accurate volume estimation in Eucalyptus plantation stands was achieved by a linear model using SPOT and Landsat multispectral imagery, specifically texture indices and pixel-scale NDVI time integrals, which reflect the local plantation growth history. Spatial modelling techniques such as Kriging with External Drift and Generalized Additive Model slightly improved predictions by accounting for spatial correlation of volume between sample points. Context: Forest inventories are widely used to estimate stand production. To capture the inherent spatial variability within stands, spatial modelling techniques such as Kriging with External Drift (KED) and the generalized additive model (GAM) have emerged. These models incorporate information on spatial correlation and auxiliary variables that can be obtained from satellite imagery. Aims: Our study explored the use of reflectance data from SPOT and Landsat multispectral imagery. We focused on texture indices and temporal integration of vegetation indices as auxiliary variables in KED and GAM to predict stem volume of fast-growing Eucalyptus sp. plantations in Brazil. Methods: The components extracted from the high-resolution SPOT-6 image included spectral band values, band ratio metrics, key vegetation indices (NDVI, SAVI, and ARVI), texture measurements, and indices derived from texture analysis. Additionally, we included the accumulated NDVI time series acquired from Landsat 5, 7, and 8 satellites between the planting date and the forest inventory measurement date. Results: The best linear model of stem volume using remotely sensed predictors gave an R-squared value of 0.95 and a Root Mean Square Error (RMSE) of 12.44 m3/ha. The R-squared increased to 0.96 and the RMSE decreased to 10.6 m3/ha when the same predictors were included as auxiliary variables in the KED and GAM spatial models. Conclusion: The linear model using remotely sensed predictors contributed most to volume prediction, but the addition of spatial coordinates in the KED and GAM spatial models improved local volume predictions. [ABSTRACT FROM AUTHOR]

Introduction: Modern forestry increasingly relies on the management of large datasets, such as forest inventories and land cover maps. Governments are typically in charge of publishing these datasets, but they typically employ disparate data formats (sometimes proprietary ones) and published datasets are commonly disconnected from other sources, including previous versions of such datasets. As a result, the usage of forestry data is very challenging, especially if we need to combine multiple datasets. Methods and results: Semantic Web technologies, standardized by the World Wide Web Consortium (W3C), have emerged in the last decades as a solution to publish heterogeneous data in an interoperable way. They enable the publication of self-describing data that can easily interlink with other sources. The concepts and relationships between them are described using ontologies, and the data can be published as Linked Data on the Web, which can be downloaded or queried online. National and international agencies promote the publication of governmental data as Linked Open Data, and research fields such as biosciences or cultural heritage make an extensive use of Semantic Web technologies. In this study, we present the result of the European Cross-Forest project, addressing the integration and publication of national forest inventories and land cover maps from Spain and Portugal using Semantic Web technologies. We used a bottom-up methodology to design the ontologies, with the goal of being generalizable to other countries and forestry datasets. First, we created an ontology for each dataset to describe the concepts (plots, trees, positions, measures, and so on) and relationships between the data in detail. We converted the source data into Linked Open Data by using the ontology to annotate the data such as species taxonomies. As a result, all the datasets are integrated into one place this is the Cross-Forest dataset and are available for querying and analysis through a SPARQL endpoint. These data have been used in real-world use cases such as (1) providing a graphical representation of all the data, (2) combining it with spatial planning data to reveal the forestry resources under the management of Spanish municipalities, and (3) facilitating data selection and ingestion to predict the evolution of forest inventories and simulate how different actions and conditions impact this evolution. Discussion: The work started in the Cross-Forest project continues in current lines of research, including the addition of the temporal dimension to the data, aligning the ontologies and data with additional well-known vocabularies and datasets, and incorporating additional forestry resources. [ABSTRACT FROM AUTHOR]

Climate change influences carbon sequestration by forests, either positively or negatively. In the Mediterranean region, this effect is predominantly negative, although it seems to be compensated by the elevation. This study aims to analyse the impact of climate change on carbon sequestration in five pine species—Pinus halepensis, P. nigra, P. pinaster, P. pinea, and P. sylvestris—growing across an altitudinal gradient from 573 to 1743 m a.s.l. in central Spain. Data from 300 forest inventory plots in ten forests were used to first calculate carbon sequestration in 2024. To estimate future carbon sequestration, the expected values of precipitation and temperature for 2100 were determined, based on regionalised climate scenarios for RCP4.5 and RCP8.5. Values from 13 meteorological stations located around the forests, at different elevations, were analysed, conducting a statistical analysis to determine whether variations were significant. A statistically significant variation was detected for temperature and precipitation changes only under the RCP8.5 scenario. Using temperature and precipitation data for 2024 and 2100, net potential productivity in both years was established, considering its variation ratio equivalent as equivalent to that of growth and carbon sequestration. An inflection point was detected in 2100 at 1646 m a.s.l., with a decrease in productivity below and an increase above that elevation. Results reflect a decline in carbon sequestration in all the species, ranging from 6% in P. sylvestris to 28% in P. halepensis, conditioned by the elevation. Regionally, the average decrease would be 16.4%. In temperate and boreal regions, forest growth is expected to increase due to climate change, but the Mediterranean region will experience a significant decrease, except in mountain areas. To maintain current levels of carbon sequestration, it would be necessary to increase the existing carbon sinks through new plantations and the restoration of degraded forests. [ABSTRACT FROM AUTHOR]

Scots pine is of greatest importance in northern Germany regarding its cultivation area and expected capability to perform in climate change. However, pine predominantly occurs in monocultures. Therefore, future pine forestry depends on an adaptation to climate change while improving ecological and economic forest functions. Yet future development of pine remains uncertain due to leeway in silvicultural guidelines and future climate. This study questions: (i) what is the range of future pine shares under climate change and different silvicultural management in northern Germany, (ii) how will the current stands develop and (iii) what is the range of uncertainty arising from climate models and silvicultural options? To answer these issues we (i) selected forest development types site- and climate-sensitively to either minimize or to maximize pine shares, (ii) simulated four, now practiced forest management scenarios for 50 years based on the German National Forest Inventory and (iii) analyzed the differences, to be interpreted as uncertainty. Novel to our approach is the site- and climate-sensitive selection of forest development types on large scales which emphasizes the contrasts of the different management guidelines. The results show that growing stock and cultivation area will decrease even if pine is promoted in forestry. The predicted restoration rate ranges from 50 to 72% depending on scenario and previous thinning regime. In conclusion, under the given management concepts and considering today's high proportion of old pine, restoration is alarmingly slow. Amid the rapidly changing climate, we recommend to further adjust the management guidelines to accelerate forest restoration. [ABSTRACT FROM AUTHOR]

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data. [ABSTRACT FROM AUTHOR]

Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMATM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection. [ABSTRACT FROM AUTHOR]

Persistence of standing dead trees (snags) is an important determinant for their role for biodiversity and dead wood associated carbon fluxes. How fast snags fall varies widely among species and regions and is further influenced by a variety of stand‐ and tree‐level factors. However, our understanding of this variation is fragmentary at best, partly due to lack of empirical data.Here, we took advantage of the accruing time series of snag observations in the Finnish, Norwegian and Swedish National Forest Inventories that have been followed in these programs since the mid‐1990s. We first harmonized observations from slightly different inventory protocols and then, using this harmonized dataset of ca. 43,000 observations that had a consistent 5‐year census interval, we modelled the probability of snags of the main boreal tree species Pinus sylvestris, Picea abies and Betula spp. falling, as a function of tree‐ and stand‐level variables, using Bayesian logistic regression modelling.The models were moderately good at predicting snags remaining standing or falling, with a correct classification rate ranging from 68% to 75% among species.In general, snag persistence increased with tree size and climatic wetness, and decreased with temperature sum, advancing stage of decay, site productivity and disturbance intensity (mainly harvesting).Synthesis and applications: The effect of harvesting demonstrates that an efficient avenue to increase the amount of snags in managed forests is protecting them during silvicultural operations. In the warmer future, negative relationship between snag persistence and temperature suggests decreasing the time snags remain standing and hence decreasing habitat availability for associated species. As decomposition rates generally increase after fall, decreasing snag persistence also implies substantially faster release of carbon from dead wood. [ABSTRACT FROM AUTHOR]

Since it is impossible in practice to create a sampling frame for the population of forest trees, forest inventories have relied on indirect sampling methods. This indirect sampling uses two populations: the discrete populations of trees and the continuous population of points, from which trees are being sampled. Important works such as Mandallaz, Eriksson, and Stevens and Urquhart brought the fundamental elements in the formalization of the sampling of trees, by defining the duality principle that relates both populations. They led to the so-called continuous population approach where trees attributes are transformed into attribute density values. However, in these approaches, the trees quickly fade away despite being the target population, while their weight is calculated as the inverse of their inclusion probability. We explain how the generalized weight share method (GWSM) can be used to formalize the link between the two populations. GWSM allows to revisit previous concepts proposed to solve the question of how to produce estimations from tree-level attributes, under uniform random or more complex sampling designs. The principles of the method are explained, and its functioning is illustrated under a variety of points and trees sampling designs, including fixed-area, Bitterlich, and cluster sampling. [ABSTRACT FROM AUTHOR]

Several studies have indicated that tri-colored bats (Perimyotis subflavus) are extending their distributional range westward in the United States. Montane and subalpine habitats consist of a mosaic of forest types including Lodgepole Pine woodlands and meadows, which provide an opportunity to study how a newly arriving species—typically associated with lowland riparian systems—is adapting to novel environmental conditions. The objectives of this study are to document Tri-colored Bat activity in these novel habitats and to quantify which factors are influencing activity patterns in habitats and at elevations not previously documented. We surveyed sites in various stages of secondary succession following beetle kill outbreaks using acoustic monitoring and nonparametric multiplicative regression to model habitat activity patterns. Results showed that activity was highest in meadows and early time-since-kill (TSK) forests in the beginning of the survey period and activity increased in late TSK forest habitats at the end of the survey in early August. Tri-colored bats are not restricting their activity to lower-elevational riparian areas in the Colorado foothills but appear to also be using these high-elevation habitats. Careful consideration of misidentification confirms the high likelihood of tri-colored bats at our sites. We hope that this study will support conservation efforts considering the expected US Fish and Wildlife Service Endangered listing for this species. [ABSTRACT FROM AUTHOR]

We describe the geographical variation in tree species composition across Amazonian forests and show how environmental conditions are associated with species turnover. Our analyses are based on 2023 forest inventory plots (1 ha) that provide abundance data for a total of 5188 tree species. Within-plot species composition reflected both local environmental conditions (especially soil nutrients and hydrology) and geographical regions. A broader-scale view of species turnover was obtained by interpolating the relative tree species abundances over Amazonia into 47,441 0.1-degree grid cells. Two main dimensions of spatial change in tree species composition were identified. The first was a gradient between western Amazonia at the Andean forelands (with young geology and relatively nutrient-rich soils) and central–eastern Amazonia associated with the Guiana and Brazilian Shields (with more ancient geology and poor soils). The second gradient was between the wet forests of the northwest and the drier forests in southern Amazonia. Isolines linking cells of similar composition crossed major Amazonian rivers, suggesting that tree species distributions are not limited by rivers. Even though some areas of relatively sharp species turnover were identified, mostly the tree species composition changed gradually over large extents, which does not support delimiting clear discrete biogeographic regions within Amazonia. Tree species turnover across Amazonian forests unveils sharp floristic transitional zones, that are linked with changes in soil fertility and climate. [ABSTRACT FROM AUTHOR]

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of WOM rates across the eastern US and explore the underlying processes behind the observed spatial patterns. Multicycle forest inventory and analysis data were compiled to capture all white oak plots. WOM data were selected across plot systems that utilized declining basal areas between two periods. Ripley's K function was used to study the spatial pattern of WOM rates. Results showed clustered patterns of WOM rates at local and broad scales that may indicate stand-level competition and regional variables affecting white oaks' dynamics across southern and northern regions. Results also indicated random patterns at broad scales, suggesting variations in topographic and hydrological conditions across the south and northern regions. However, the central region indicated both clustered and random patterns at the local scale that might be associated with inter-species competition and the possibility of environmental heterogeneity, respectively. Furthermore, uniform patterns of WOM rate at a broad scale across all regions might suggest regions with spatially homogeneous environmental factors acting on the dynamics of white oaks. This research might be helpful in identifying impacted areas of white oaks at varying scales. Future research is needed to comprehensively assess biotic and abiotic factors at various spatial scales aimed at mitigating WOM. [ABSTRACT FROM AUTHOR]

Tree diameter at breast height (DBH) can visually reflect the size of trees and is closely related to forest carbon sinks. As its continuous change is affected by the growing environment, it is an important indicator for forest surveys, and it is of great value for forest carbon economy and microecological research. In order to realize the accurate and continuous digital measurement of tree diameter at breast height, this paper develops a low-power tree diameter at breast height continuous measurement device based on the magneto-resistive effect. Compared to the traditional method of tree diameter measurement, this device has the advantages of real-time transmission of data, low-cost, anti-interference, and so on. In Zhejiang Jinhua Nanshan Nature Reserve, at a number of sample sites, tree diameter at breast height for 10 different species of trees was measured every 16 h before developing the corresponding upper software, background analysis software, and real-time acceptance of the measurement data to make timely analysis. After 12 months, experimental application and data analysis showed that the measurement accuracy of this device can be up to 0.001 mm. Compared to traditional tree diameter ruler measurement, measurement error is within 0.1%. This device, therefore, enables the continuous measurement, transmission, storage, and analysis of the tree diameter as a whole, and reveals the growth and carbon sink change rules of the tree diameter of a specific region at a certain age. [ABSTRACT FROM AUTHOR]

Research related to Cerradão vegetation focuses more on the floristic-structural aspect, with rare studies on the quantification of volume and biomass stocks, and even fewer investigating the increments of these attributes. Using a systematic sampling method with subdivided strips and 400 m2 plots, the density found was 1135, 1165, and 1229 trees/ha in 2012, 2020, and 2023, respectively, in Lajeado State Park, Tocantins State, Brazil. Volume was estimated using the equation v = 0.000085 D 2.122270 H 0.666217 , and biomass was estimated using the equation A G B = 0.0673 ρ D 2 H 0.976 . Vegetation dynamics were assessed using growth increment, recruitment, mortality, turnover rate, and time. The results indicated that dynamics have increased since the start of monitoring. Typical Cerrado species, in the strict sense, were replaced by those from forest environments. The total production in volume and biomass was 160.91 m3/ha and 118.10 Mg/ha, respectively, in 2023. The species of Emmotum nitens, Mezilaurus itauba, Ocotea canaliculata, and Sacoglottis guianensis showed the highest increment values in volume and biomass. For the community, the average values were 4.04 m3/ha/year and 3.54 Mg/ha/year. The community has not yet reached its carrying capacity and stores a significant amount of biomass. This is influenced by the transition of the study area from an exploited environment to a conservation unit (park) and by its location in a transitional area with the Amazon biome. [ABSTRACT FROM AUTHOR]

The Cerrado has high plant and vertebrate diversity and is an important biome for conserving species and provisioning ecosystem services. Volume equations in this biome are scarce because of their size and physiognomic diversity. This study was conducted to develop specific volumetric models for the phytophysiognomies Gallery Forest, Dry Forest, Forest Savannah, and Savannah Woodland, a generic model and a model for Cerrado forest formation. Twelve 10 m × 10 m (100 m²) (National Forest Inventory) plots were used for each phytophysiognomy at different sites (regions) of the Federal District (FD) where trees had a diameter at breast height (DBH; 1.30 m) ≥5 cm in forest formations and a diameter at base height (Db; 0.30 m) ≥5 cm in savanna formations. Their diameters and heights were measured, they were cut and cubed, and the volume of each tree was obtained according to the Smalian methodology. Linear and nonlinear models were adjusted. Criteria for the selection of models were determined using correlation coefficients, the standard error of the estimates, and a graphical analysis of the residues. They were later validated by the chi-square test. The resultant models indicated that fit by specific phytophysiognomy was ideal; however, the generic and forest formation models exhibited similar performance to specific models and could be used in extensive areas of the Cerrado, where they represent a high potential for generalization. To further increase our understanding, similar research is recommended for the development of specific and generic models of the total volume in Cerrado areas. [ABSTRACT FROM AUTHOR]

Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources. And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing. Based on GF-2 remote sensing data, sample plot survey data and forest resource survey data, this study used the Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) and Pinus massoniana Lamb. as research objects to tackle the key challenges in the use of remote sensing technology. The Boruta feature selection technique, together with multiple stepwise and Cubist regression models, was used to estimate crown density and volume in portions of the research area's stands, introducing novel technological methods for estimating stand parameters. The results show that: (i) the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable, which solves the problem of data and the loss of original feature data after dimensionality reduction; (ii) using the Cubist method to build the model yields better results than using multiple stepwise regression. The Cubist regression model's coefficient of determination (R 2) is all more than 0.67 in the Chinese fir plots and 0.63 in the P. massoniana plots. As a result, combining the two methods can increase the estimation accuracy of stand parameters, providing a theoretical foundation and technical support for future studies. [ABSTRACT FROM AUTHOR]

Accurate and efficient forest inventories are essential for effective forest management and conservation. The advent of ground-based remote sensing has revolutionized the data acquisition process, enabling detailed and precise 3D measurements of forested areas. Several algorithms and methods have been developed in the last years to automatically derive tree metrics from such terrestrial/ground-based point clouds. However, few attempts have been made to make these automatic tree metrics algorithms accessible to wider audiences by producing software solutions that implement these methods. To fill this major gap, we have developed 3DFin , a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds. 3DFin empowers users to automatically compute key forest inventory parameters, including tree Total Height, Diameter at Breast Height (DBH), and tree location. To enhance its user-friendliness, the program is open-access, cross-platform, and available as a plugin in CloudCompare and QGIS as well as a standalone in Windows. 3DFin capabilities have been tested with Terrestrial Laser Scanning, Mobile Laser Scanning, and terrestrial photogrammetric point clouds from public repositories across different forest conditions, achieving nearly full completeness and correctness in tree mapping and highly accurate DBH estimations (root mean squared error <2 cm, bias <1 cm) in most scenarios. In these tests, 3DFin demonstrated remarkable efficiency, with processing times ranging from 2 to 7 min per plot. The software is freely available at: https://github.com/3DFin/3DFin. [ABSTRACT FROM AUTHOR]

Satellite data are increasingly used to provide information to support forest monitoring and reporting at varying levels of detail and for a range of attributes and spatial extents. Forests are dynamic environments and benefit from regular assessments to capture status and changes both locally and over large areas. Satellite data can provide products relevant to forest science and management on a regular basis (e.g. annually) for land cover, disturbance (i.e. date, extent, severity, and type), forest recovery (e.g. quantification of return of trees following disturbance), and forest structure (e.g. volume, biomass, canopy cover, stand height), with products generated over large areas in a systematic, transparent, and repeatable fashion. While pixel-based outcomes are typical based upon satellite data inputs, many end users continue to require polygon-based forest inventory information. To meet this information need and have a spatial context for forest inventory attributes such as tree species assemblages, we present a new work-flow to produce a novel spatially explicit, stand-level satellite-based forest inventory (SBFI) in Canada applying image segmentation approaches to generate spatially unique forest stands (polygons), which are the fundamental spatial unit of management-level inventories. Thus, SBFI offers spatial context to aggregate and generalize other pixel-based forest data sets. Canada has developed a National Terrestrial Ecosystem Monitoring System (NTEMS) that utilizes medium spatial resolution imagery, chiefly from Landsat, to annually characterize Canada's forests at a pixel level from 1984 until present. These NTEMS datasets are used to populate SBFI polygons with information regarding status (e.g. current land cover type, dominant tree species, or total biomass) as well as information on dynamics (e.g. has this polygon been subject to change, when, by what, and if so, how is the forest recovering). Here, we outline the information drivers for forest monitoring, present a set of products aimed at meeting these information needs, and follow to demonstrate the SBFI concept over the 650-Mha extent of Canada's forest-dominated ecosystems. In so doing, the entirety of Canada's forest ecosystems (managed and unmanaged) were mapped using the same data, attributes, and temporal representation. Moreover, the use of polygons allows for the generation of attributes such as tree species composition, and total biomass and wood volume in a stand-scale format familiar to landscape managers and suitable for strategic planning. The data, methods, and outcomes presented here are portable to other regions and input data sources, and the national SBFI outcomes for Canada are available via open access. [ABSTRACT FROM AUTHOR]

Tree productivity is not only determined by stand structure, but also influenced by soil chemical properties, climate and topography. However, the relative importance of each indicator on larch (Larix principis-rupprechtii) productivity were uncertain. In this study, 76 pure larch forest plots were selected based on national forest inventory (NFI) data in Hebei Province, China. Structural equation model (SEM) was used to analyze the direct and indirect effects of stand structure, soil chemical properties, climate and topography on larch productivity, and to quantify the relative importance of each indicator in determining productivity. The results showed that stem volume growth (SVG) of larch was influenced by a combination of stand density, diameter at breast height (DBH), mean winter snow (PAS), annual temperature range (TD), slope, and alkali-hydrolysis nitrogen (AN). SVG tended to increase with decreasing stand density and AN content and increasing DBH. Stand density, DBH and AN were more important than PAS, TD, and slope in explaining SVG variation. The results can provide a scientific basis for adaptive management of larch forests. [ABSTRACT FROM AUTHOR]

Forest productivity is a crucial integrator of ecosystem functions and services. Although the effects of landscape structure on species richness and stand structure have been extensively studied, how landscape structures affect forest productivity and their interactions with stand‐level attributes, especially in the context of considerable land use change, remain unclear. In this study, we investigated the effects of landscape structures (fragmentation, complexity, and heterogeneity) and their interactions with stand‐level attributes on forest productivity in the conterminous United States across three spatial scales (1–3 km), using an extensive forest inventory dataset from the national forest inventories (NFI) plots. Our results revealed that all landscape indices around selected forest plots significantly increased from 2006 to 2016. Across three scales, forest productivity and stand‐level attributes (number of trees, tree species richness, and structural diversity) exhibited unimodal relationships with landscape fragmentation and complexity, while generally showed positive correlations with landscape heterogeneity. The interactions between landscape structures and stand attributes enhanced the explanatory power of forest productivity. Landscape complexity directly or indirectly reduced forest productivity by decreasing the number of trees and tree species richness, whereas landscape fragmentation and heterogeneity had the opposite effects. Furthermore, landscape heterogeneity and stand age had relatively stronger total effects (the sum of direct and indirect effects) on forest productivity, and their strength increased modestly with spatial scales. However, tree species richness consistently had the lowest total effects. Our study elucidates the complex driving mechanisms of landscape patterns on forest productivity across spatial scales, providing a deeper understanding of ecosystem complexity and responses to accelerating land use changes. [ABSTRACT FROM AUTHOR]

Forest ecosystems are long-term exposed to dry periods in Europe, which leads to a significant loss of vitality and higher mortality, especially in coniferous forests. Identifying stress in the early stages when measures can be taken to protect the forest and living trees is crucial. Current detection methods are based on field surveys by forest workers or remote sensing methods to cover larger areas, which use changes in spectral reflectance of the forest canopy. In some cases, the attacked trees do not change their appearance, and based on calculations of vegetation indices from remote sensing data, the attack cannot be mapped. We present an innovative methodology based on non-optical analysis, namely identifying a group of volatile compounds and microclimate signs in forest stands that indicate stress factors in forest stands. An attacked tree by a bark beetle produces increased amounts of biogenic volatile organic compounds associated with defense, and the microclimate changes due to interrupted transpiration. In addition, the bark beetle uses the aggregation pheromone to attract more individuals and to attack the tree massively. In this study, we tested three electronic noses (Miniature Bosch sensor device with 25,419 samples, Sensory device for environmental applications with 193 samples, Handheld VOC Detector Tiger with 170 samples) in a freshly infested spruce stand. The measurement was conducted at ground level with the help of a human operator and was repeated six times to verify the detection capability of the electronic noses. To verify the capability of electronic noses to predict tree infestation, we used machine learning Random Forest. The results demonstrated that electronic noses can detect bark beetle infestation start (within 1 week of the first attack). The Miniature Bosch sensor device achieved the highest accuracy with a value of 95%, in distinguishing forest sections that are healthy and infested; the second most accurate electronic nose is the Sensory device for environmental applications, with an accuracy of 89%. Our proposed methodology could be used to detect bark beetle presence. [ABSTRACT FROM AUTHOR]

Key message: Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees. Context: The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration. Aims: The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots. Methods: Tree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression. Results: Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data. Conclusion: The MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions. [ABSTRACT FROM AUTHOR]

Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects. Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height. Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species. Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution. [ABSTRACT FROM AUTHOR]

We address a Bayesian two-stage decision problem in operational forestry where the inner stage considers scheduling the harvesting to fulfill demand targets and the outer stage considers selecting the accuracy of pre-harvest inventories that are used to estimate the timber volumes of the forest tracts. The higher accuracy of the inventory enables better scheduling decisions but also implies higher costs. We focus on the outer stage, which we formulate as a maximization of the posterior value of the inventory decision under a budget constraint. The posterior value depends on the solution to the inner stage problem and its computation is analytically intractable, featuring an NP-hard binary optimization problem within a high-dimensional integral. In particular, the binary optimization problem is a special case of a generalized quadratic assignment problem. We present a practical method that solves the outer stage problem with an approximation which combines Monte Carlo sampling with a greedy, randomized method for the binary optimization problem. We derive inventory decisions for a dataset of 100 Swedish forest tracts across a range of inventory budgets and estimate the value of the information to be obtained. [ABSTRACT FROM AUTHOR]

The mountains of northern Kenya (Ndoto, Nyiro, Kulal and Marsabit) are separated by semi‐arid plains uninhabitable for most terrestrial molluscs (snails and slugs). Nevertheless, each "sky island" supports a little‐known forest mollusc fauna. The diversity and endemism of these faunas are addressed here based on surveys and the limited records available in the literature. In total 75 forest mollusc species and subspecies were found, including six previously undescribed Streptaxidae and many first records. Most previously recorded taxa were refound. The richness of faunas is broadly comparable to that of other Kenyan forests surveyed using similar methods (Ndoto 41 taxa, Nyiro 40, Kulal 33, Marsabit 34). A large proportion of the forest taxa (24 taxa, 29%) are endemic to Kenya north of the Mathews Range. More than half of these (14 taxa, 17% of the total) are known only from a single mountain range. At Nyiro, where different forest types were compared, fewer endemic species were found in Juniper forest than in other forest types. Moving northwards along the Ndoto‐Nyiro‐Kulal chain, diversity declines while endemism increases, perhaps reflecting increasing isolation from the southern forests, but the trends are not significant when Marsabit is included. Across the four ranges, neither diversity nor endemism is clearly linked to the isolation, maximum elevation, forest cover or geological origin of each range (Kulal and Marsabit are volcanoes, while Nyiro and Ndoto are uplifted basement rock). Instead, the unique circumstances of each range create a distinctive set of isolated faunas. [ABSTRACT FROM AUTHOR]

Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harvesters that can provide high-resolution forestry yield data. The objectives of this study were to present a method for generating high-resolution Eucalyptus grandis yield data (individual tree-level) and explore their applications, such as correlation analysis with soil attributes to aid nutrient recommendations. Two evaluations were conducted at two sites in Brazil: (a) assessing the positioning accuracy of the global navigation satellite system (GNSS) receiver positioning, and (b) analyzing the yield data and their correlation with the soil attributes. The results indicated that positioning the GNSS receiver at the harvesting head provided higher accuracy than placement at the top of the harvester cabin for individual tree-level data. Reliable yield data were generated despite the GNSS receiver's increased susceptibility to damage when mounted on a harvest head. The linear correlation analysis between the Eucalyptus grandis yield data and soil attributes showed both negative (Clay, B, S, coarse sand, and potential acidity − H + Al) and positive correlations (K, Mg, pH-SMP, Ca, sum of bases, pH, base saturation, fine sand, total sand, and silt content). This study demonstrates the feasibility of obtaining high-resolution yield data at the individual tree-level and their correlation with soil attributes, providing valuable insights for improving forestry decision-making. [ABSTRACT FROM AUTHOR]

Background: The inventory of street tree populations has acquired new importance due to interest in the provision of ecosystem services. That said, this paper aims to compare systematic sampling with stratified systematic sampling using different sizes of sampling units to estimate the variables of interest: number of trees per kilometer of sidewalk (DF), basal area per kilometer of sidewalk (Dg), mean total height, volume per kilometer of sidewalk (DV), and number of species per kilometer of sidewalk (DE). An innovative contribution here is testing new alternative density variables. Methods: In the densely urbanized area of Piracicaba (Sao Paulo State, Brazil), 90 sets of 4 blocks were systematically sampled. They were used to compose sampling units of 1, 2, 3, and 4 blocks. Stratification was based on the percentage of street tree cover obtained with geoprocessing tools. Only public trees with a circumference at breast height greater than or equal to 12 cm and planted on sidewalks or avenue medians were included. Results: The effect of sampling unit size and stratification on estimate accuracy, sample size, and sampling intensity were analyzed. The results show that stratified systematic sampling was the more accurate process, especially for DF, Dg, and DV. Conclusions: Reductions in sample size were more significant when stratified systematic sampling of 2-block sampling units were used. [ABSTRACT FROM AUTHOR]

Land-use change (LUC) impacts biospheric carbon, encompassing biomass carbon and soil organic carbon (SOC). Despite the use of dynamic global vegetation models (DGVMs) in estimating the anthropogenic perturbation of biospheric carbon stocks, critical evaluations of model performance concerning LUC impacts are scarce. Here, we present a systematic evaluation of the performance of the DGVM Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) in reproducing observed LUC impacts on biospheric carbon stocks over Europe. First, we compare model predictions with observation-based gridded estimates of net and gross primary productivity (NPP and GPP), biomass growth patterns, and SOC stocks. Second, we evaluate the predicted response of soil carbon stocks to LUC based on data from forest inventories, paired plots, chronosequences, and repeated sampling designs. Third, we use interpretable machine learning to identify factors contributing to discrepancies between simulations and observations, including drivers and processes not resolved in ORCHIDEE (e.g. erosion, soil fertility). Results indicate agreement between the model and observed spatial patterns and temporal trends, such as the increase in biomass with age, when simulating biosphere carbon stocks. The direction of the SOC responses to LUC generally aligns between simulated and observed data. However, the model underestimates carbon gains for cropland-to-grassland conversions and carbon losses for grassland-to-cropland and forest-to-cropland conversions. These discrepancies are attributed to bias arising from soil erosion rate, which is not fully captured in ORCHIDEE. Our study provides an oriented benchmark for assessing the DGVMs against observations and explores their potential in studying the impact of LUCs on SOC stocks. [ABSTRACT FROM AUTHOR]

The biogeography of irruptive insect herbivores is determined by host availability and climate conditions. As such, outbreak distributions are sensitive to climatic change, especially across large latitudinal gradients. Here, we investigate the outbreak distributions of two understudied defoliators, hemlock sawfly Neodiprion tsugae (Hymenoptera) and western blackheaded budworm Acleris gloverana (Lepidoptera), that have both recently impacted the greatest land area recorded across the Pacific coastal temperate rainforest since the establishment of aerial survey programs. We compiled polygon‐based estimates of insect damage collected by aerial observers, forest inventory, and downscaled climatic data to develop gridded estimates of bioclimatic conditions across the extent of the Pacific coastal temperate rainforest, including the continental United States, British Columbia and Alaska. We leveraged these data to develop ensemble machine learning models with the goal of predicting the outbreak distribution of each insect. In this manuscript we: 1) describe the historical patterns of defoliator outbreaks, 2) identify and describe climatic conditions associated with outbreaks in both species and 3) assess whether historic outbreaks have tracked geographic shifts in climate conditions across the region. We demonstrate that outbreaks of hemlock sawfly and western blackheaded budworm have been observed across the Pacific coastal temperature rainforests of North America in each decade since the establishment of the Canadian and United States aerial survey programs. The distribution of outbreaks by both insects were best explained by host availability, a limited range of spring, summer, and winter temperatures, and minimum precipitation. Finally, we demonstrate that outbreaks have tracked the poleward shift in suitable climate over the last century. This study establishes a baseline understanding of the climatic constraints and biogeographic patterns of historic sawfly and budworm outbreaks across the Pacific coastal temperate rainforest and emphasizes the overarching importance of climate in driving the irruptive dynamics of these defoliator species. Keywords: climate envelope, defoliators, hemlock sawfly, Pacific coastal temperate rainforest, population dynamics, western blackheaded budworm [ABSTRACT FROM AUTHOR]

The characteristics of pre-industrial forests provide reference states for forest restoration and sustainable management. This historical approach is particularly relevant in highly human-impacted regions where natural forests that could serve as references are scarce. This is the case in the Rouyn-Noranda region, located in western Quebec. In this study, we used available survey archives to reconstruct the pre-industrial composition of the mixed boreal forests in the Rouyn-Noranda region, located in western Quebec. With a new database comprising 3621 observations on historical forest composition (1909–1940), we provide an overview of the pre-industrial forests and subsequent changes in the region. In the pre-industrial era, spruces represented the most abundant species: they were present in 85.5% of the observations and were identified as dominant (i.e., the most abundant locally) in 63.9% of the observations. Spruces tended to be abundant across the entire territory and surface deposit types. Our results show an increase in the frequency of early successional deciduous species, primarily trembling aspen (+28%), between the pre-industrial and modern eras (1980–2020). These composition changes appear to be attributable to a combination of natural disturbances (fires) and human activities (logging, agriculture, etc.). We finally discuss the implications of our results for sustainable forest management strategies in the region. [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring. [ABSTRACT FROM AUTHOR]

Tropical forests play an important role in mitigating global climate change, emphasizing the need for reliable estimates of forest carbon stocks at regional and global scales. This is essential for effective carbon management, which involves strategies like emission reduction and enhanced carbon sequestration through forest restoration and conservation. However, reliable sample-based estimations of forest carbon stocks require accurate allometric equations, which are lacking for the rainforests of the Atlantic Forest Domain (AFD). In this study, we fitted biomass equations for the three main AFD forest types and accurately estimated the amount of carbon stored in their above-ground biomass (AGB) in Rio de Janeiro state, Brazil. Using non-destructive methods, we measured the total wood volume and wood density of 172 trees from the most abundant species in the main remnants of rainforest, semideciduous forest, and restinga forest in the state. The biomass and carbon stocks were estimated with tree-level data from 185 plots obtained in the National Forest Inventory conducted in Rio de Janeiro. Our locally developed allometric equations estimated the state's biomass stocks at 70.8 ± 5.4 Mg ha−1 and carbon stocks at 35.4 ± 2.7 Mg ha−1. Notably, our estimates were more accurate than those obtained using a widely applied pantropical allometric equation from the literature, which tended to overestimate biomass and carbon stocks. These findings can be used for establishing a baseline for monitoring carbon stocks in the Atlantic Forest, especially in the context of the growing voluntary carbon market, which demands more consistent and accurate carbon stock estimations. [ABSTRACT FROM AUTHOR]

Forests are an important part of natural resources and play an important role in carbon sinks. We measured carbon sinks in provincial forest parks using data from four forest inventory surveys in China and the forest stock expansion method. Carbon emissions from forest tourism were also estimated using energy statistics and forest park tourism data. On this basis, spatial analysis was used to summarize the spatial and temporal evolution of the carbon balance and the analysis of influencing factors. The results show the following: (1) With the passage of time, the carbon emissions from forest tourism in all provinces have increased to different degrees, and the national forest tourism carbon emissions have increased from 1,071,390.231 (million tons) in 2003 to 286,255,829.7 (million tons) in 2018; spatially, the distribution of carbon emissions from forest tourism is uneven, with an overall high in the south and low in the north, and a high in the east and a low in the west. (2) The carbon sink of forest parks showed a trend of gradual growth and spatially formed a spatial pattern of high in the northeast and low in the southwest, which is consistent with the distribution of forest resources in China. (3) For forest tourism carbon emissions, the total number of tourists, tourism income, and playing roads are significant influencing factors, and the baseline regression coefficients are 0.595, 0.433, and 0.799, respectively, while for forest park carbon sinks, the number of forest park employees can play a certain positive role in carbon sinks, with the regression coefficient being 1.533. [ABSTRACT FROM AUTHOR]

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects. [ABSTRACT FROM AUTHOR]

Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau), which is native to China, is considered to be an invasive species due to its powerful asexual reproductive capabilities that allow it to rapidly spread into neighboring ecosystems and replace existing plant communities. In the absence of human intervention, it remains poorly understood how indigenous moso bamboo forests naturally expand into surrounding areas over the long term, and whether these patterns vary with environmental changes. Using multi-year forest resource inventory data, we extracted moso bamboo patches that emerged from 2010 to 2020 and proposed a bamboo expansion index to calculate the average rate of patch expansion during this period. Using the first global 30 m land-cover dynamic monitoring product with a fine classification system, we assessed the expansion speeds of moso bamboo into various areas, particularly forests with different canopy closures and categories. Using parameter-optimized geographic detectors, we explored the significance of multi-factors in the expansion process. The results indicate that the average expansion rate of moso bamboo forests in China is 1.36 m/y, with evergreen broadleaved forests being the primary area for invasion. Moso bamboo expands faster into open forest types (0.15 < canopy closure < 0.4), shrublands, and grasslands. The importance of factors influencing the expansion rate is ranked as follows: temperature > chemical properties of soil > light > physical properties of soil > moisture > atmosphere > terrain. When considering interactions, the primary factors contributing to expansion rates include various climate factors and the combined effect of climate factors and soil factors. Our work underscores the importance of improving the quality and density of native vegetation, such as evergreen broadleaved forests. Effective management strategies, including systematic monitoring of environmental variables, as well as targeted interventions like bamboo removal and soil moisture control, are essential for mitigating the invasion of moso bamboo. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The standardized methods used in carbon markets require measurement of the biomass and carbon stored in trees, which can be quantified through allometric equations. The objective of this study was to analyze aboveground biomass estimates with allometric models in three mangrove species and compare them with those used by the Climate Action Reserve (CAR) standard. The mangrove forest in Tabasco, Mexico, was certified with the Forest Protocol for Mexico Version 2.0 (FPM) of the CAR standard. Allometric equations for mangrove species were reviewed to determine the most suitable equation for the calculation of biomass. The predictions of the allometric equations of the FPM were analyzed with data from Tabasco from the National Forest and Soil Inventory 2015–2020, and the percentages of trees within the ranges of diameters of the FPM equations were determined. The FPM equations generated higher biomass values for Rhizophora mangle and lower values for Avicennia germinans than the seven equations with which they were compared. In the mangrove swamp of Ejido Úrsulo Galván, Tabasco, 81.8% of the biomass of A. germinans, 34.4% of Laguncularia racemosa and 24.0% of R. mangle were within the diameter range of the FPM equations, and in Tabasco, 28.5% of A. germinans, 16.7% of L. racemosa and 5.7% of R. mangle were within the diameter range. For A. germinans and R. mangle, we recommend using the equation that considers greater maximum diameters. The allometric equations of the FPM do not adequately predict a large percentage of the biomass. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)