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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.

Creating maps of forest inventory variables is commonly taking advantage of satellite images, which are mosaicked together for gaining larger coverage. Recently, mosaicking has increasingly shifted towards user friendly cloud-based online environments such as Google Earth Engine (GEE), which are equipped with huge image repositories and extensive processing capabilities. This enables the easy transferability of workflows into new image sets and diversifies the range of methodological options for mosaicking. The quality control of the output mosaic, ensuring that the reflectance values are representative to the targeted land cover, is however primarily based on certain assumptions or pre-set rules which may not always produce an optimal result. Our study focuses on assessing and comparing the performance of three different mosaicking algorithms for predicting forest inventory variables, based on an extensive set of field data on the main site type, fertility class, and volume and biomass of growing stock. One of the compared mosaics derives from manual image selection, thus enabling rigorous visual quality control, and two others are resting on GEE-assisted automatized methods which include applying a percentile-based statistic over all the input reflectance values and selecting the best pixels using predefined quality indicators. The results indicate that the manual and the percentile-based mosaics are generally providing the best and relatively equal performance levels. Compared to them, the quality-based mosaic has slightly lower accuracy particularly when predicting continuous variables (i.e., the volume and biomass of growing stock) and it suffers from minor image defects. For the total volume of growing stock, for example, the RMS errors are 56.22 % for the manual, 56.33 % for the percentile-based, and 59.47 % for the quality-based mosaics, respectively. These results indicate that from the perspective of large area forest mapping, automatically generated mosaics may provide approximately similar accuracy as compared to manually controlled workflow at a fraction of the workload.

IntroductionIn situ carbon sequestration in forests is important in the context of climate change mitigation, and setting aside managed forests has been proposed as an option for increased carbon sequestration. Comparing set-aside and managed forests may provide insights and rules of thumb on the potential for additional in situ carbon sequestration in set-aside forest.MethodsIn an observational study, we compared re-inventory data from the network of set-aside forest reserves in Flanders, which have been unmanaged for 17–66 years (2 surveys with a 10 years interval), with re-inventory data from the regional forest inventory, representing the overall forest area in Flanders (2 surveys with a 15 years interval).ResultsThe aboveground carbon pools and sequestration rates were higher in the set-aside forests compared to the average forest in Flanders. In the average Flemish forest, the aboveground carbon pool increased from 64.7 to 85.1 tC ha−1, over a period of 15 years. In the set-aside forests, the mean pool was higher at the first measurement and further increased from 84.8 to 102.4 tC ha−1, over a period of 10 years. The mean aboveground annual carbon sequestration rate was 1.3 tC ha−1 year−1 in the average forest in Flanders and 1.8 tC ha−1 year−1 in the set-aside forests. The stocks and fluxes depended on the soil conditions and were higher in set-aside forests on silt and sandy silt sites compared to wet and sand sites. The set-aside forests on dry sites showed additionality in in situ aboveground carbon sequestration. We saw no indication of approaching a culmination point in the first decades following set-aside: plots with high carbon pools did not show lower carbon sequestration. In conclusion, set-aside forests can combine high carbon pools with high sequestration rates on suitable sites. Under the current management policy, we expect Flemish forests—regular and set-aside—to further increase their carbon pools in the coming decades.

Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to ‘translate’ one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

The quantity and spatial patterns of aboveground biomass (AGB) are expected to correlate with ecosystem structure and biodiversity across biomes. However, the local and regional variations in the strength of such relationships remain poorly understood partly due to the influence of ecosystem disturbances, such as land-use change. Here, we quantified AGB in tropical montane cloud forest (TMCF) in southern Mexico and analyzed its distribution patterns at local and regional scales. Specifically, we investigated how land use and environmental factors (that is, topography and climate) influence AGB spatial patterns and the relationship between forest structure, AGB, and tree species diversity across forests with different levels of disturbance, using 160 plots from the Mexican National Forest Inventory (FI) database. Our results show that AGB (averaging 137 Mg ha−1) is strongly influenced by variations in forest structure such as stand basal area and the density of large trees, with a weak but positive relation with tree species diversity. AGB increased with elevation and slope and decreased with very high levels of precipitation and land-use intensity, suggesting that spatial variation in AGB across the region can be best predicted by the interactive effects of land use and environmental factors, with land use having a larger role. Our results challenge general assumptions about the structural and compositional properties of montane forest ecosystems and emphasize the need to explicitly include interactions between environmental and human drivers when analyzing changes in AGB and devising sustainable management plans. [ABSTRACT FROM AUTHOR]

Many nations administer national forest inventory programs for unbiased estimation of forest attributes over broad spatial and temporal extents. However, management and conservation decisions often demand reliable estimates for finer spatiotemporal domains. In the western US, wildfire activity is expanding and postfire regeneration must contend with a warmer, drier climate. We evaluate the potential of K nearest neighbor (KNN) strategies for estimation of stocking across postfire measurements of Forest Inventory & Analysis plots in 11 western US states, and subsequently for model-assisted (MA) estimation of stocking over domains defined by aggregations of burned areas within individual states and 4-year periods. In particular, we develop and evaluate a form of constrained KNN that allows for unbiased MA domain estimation under simple random sampling by drawing only on measurements external to a domain of interest. KNN strategies based on geographically, radiometrically, and climatically proximate measurements are found to provide more accurate estimates of stocking at the plot level than domain means. Applying the selected external KNN strategy also reduced standard errors of MA domain estimates by 16% over direct domain estimators, but bias correction introduces substantial variability over synthetic estimates. Further applications of the external constraint imposed on KNN are discussed. [ABSTRACT FROM AUTHOR]

Transpiration (Et) in forests accounts for a significant fraction of evapotranspiration. However, because Et is influenced by meteorological factors and the physiological response of vegetation, developing an accurate model for predicting Et presents a challenge. In this study, we developed a model to estimate Et specific to cypress plantation forests using satellite remote sensing and sap flow data in Japanese cypress plantations composed of a single forest species. The contribution of Et to meteorological (Et base) and plant physiology (Et leaf) was determined using the decoupling index along with the surface temperature. Using the Forest Inventory (FI), in which the extent of growth of each species, including cypress, was surveyed and Land Remote Sensing Satellite imagery of the FI range was obtained, an index of activity estimation for cypress trees was developed. The sapwood area, which significantly influences Et, was calculated from the tree age obtained from FI or diameter at breast height and incorporated into the model. The developed model exhibited a high correlation of r = 0.74–0.88 with the measured Et. The antecedent precipitation index (API), which represents the wetness of the land surface, can be used to evaluate the confidence level and correction coefficient (if needed) of model estimates. When corrected by API, Et could be estimated with even higher accuracy (r = 0.76–0.89). The model developed in this study considers the physiological responses of vegetation and plant species, and the same approach can be used to develop models to estimate Et for other tree species. Additionally, our model uses reflectance at wavelengths close to visible light and surface temperature and can, therefore, be readily applied to other remote sensing methods, such as unmanned aerial vehicles and airborne methods. The model developed in this study can be used with FI data to estimate Et by tree species to provide detailed and accurate estimates of Et. [ABSTRACT FROM AUTHOR]

The study of the forest structure makes it possible to solve many important problems of forest inventory. LiDAR scanning is one of the most widely used methods for obtaining information about a forest area today. To calculate the structural parameters of plantations, a reliable segmentation of the initial data is required, the quality of segmentation can be difficult to assess in conditions of large volumes of forest areas. For this purpose, in this work, a system of correctness and quality of segmentation was developed using deep learning models. Segmentation was carried out on a forest area with a high planting density, using a phased segmentation of layers using the DBSCAN method with preliminary detection of planting coordinates and partitioning the plot using a Voronoi diagram. The correctness model was trained and tested on the extracted data of individual trees on the PointNet ++ and CurveNet neural networks, and good model accuracies were obtained in 89 and 88%, respectively, and are proposed to use the quality assessment of clustering methods, as well as improve the quality of LiDAR data segmentation on separate point clouds of forest plantations by detecting frequently occurring segmentation defects. [ABSTRACT FROM AUTHOR]

Rapid and precise tree Diameter at Breast Height (DBH) measurement is pivotal in forest inventories. While the recent advancements in LiDAR and Structure from Motion (SFM) technologies have paved the way for automated DBH measurements, the significant equipment costs and the complexity of operational procedures continue to constrain the ubiquitous adoption of these technologies for real-time DBH assessments. In this research, we introduce KAN-Forest, a real-time DBH measurement and key point localization algorithm utilizing RGB-D (Red, Green, Blue-Depth) imaging technology. Firstly, we improved the YOLOv5-seg segmentation module with a Channel and Spatial Attention (CBAM) module, augmenting its efficiency in extracting the tree's edge features in intricate forest scenarios. Subsequently, we devised an image processing algorithm for real-time key point localization and DBH measurement, leveraging historical data to fine-tune current frame assessments. This system facilitates real-time image data upload via wireless LAN for immediate host computer processing. We validated our approach on seven sample plots, achieving b b A P 50 and s e g A P 50 scores of: 90.0 % (+ 3.0 %) , 90.9 % (+ 0.9 %) , respectively with the improved YOLOv5-seg model. The method exhibited a DBH estimation RMSE of 17.61∼54.96 mm ( R 2 = 0.937 ), and secured 78% valid DBH samples at a 59 FPS. Our system stands as a cost-effective, portable, and user-friendly alternative to conventional forest survey techniques, maintaining accuracy in real-time measurements compared to SFM- and LiDAR-based algorithms. The integration of WLAN and its inherent scalability facilitates deployment on Unmanned Ground Vehicles (UGVs) to improve the efficiency of forest inventory. We have shared the algorithms and datasets on Github for peer evaluations. [ABSTRACT FROM AUTHOR]

The Hyrcanian forest is a global biodiversity hotspot that harbors many endemic and endangered tree species, but its tree diversity is threatened by various human-induced disturbances, such as logging, grazing, and urbanization. To address this issue, we conducted a study using three machine learning methods, i.e., linear regression (LR), random forest (RF), and support vector machine (SVM), to assess and predict tree species diversity within the forest. To do so, we collected an extensive dataset of forest structure and environmental factors from 2725 sample plots located throughout the forest. The Shannon–Wiener diversity index was used to quantify the tree species diversity for each plot. We found that basal area, tree density, and height of trees were the most important predictors of tree diversity, followed by diameter at breast height, elevation, slope, and aspect. We measured the performance of the models using the coefficient of determination (R2), root mean square error (RMSE), and percent of relative error index (PREI), and found RF as the best-performing model in both the training (RMSE = 0.143, R2 = 0.94, and PREI = − 0.09) and validation (RMSE = 0.15, R2 = 0.94, and PREI = − 0.09) phases. RF was able to generalize effectively to new data without losing much accuracy or explanatory power. SVM demonstrated a moderate performance training (training phase: RMSE = 0.23, R2 = 0.57, and PREI = − 0.17) and (validation phase: RMSE = 0.36, R2 = 0.34, and PREI = − 0.21) among the models, while LR performed the worst (training phase: RMSE = 0.41, R2 = 0.13, and PREI = − 0.19) and (validation phase: RMSE = 0.41, R2 = 0.11, and PREI = − 0.36). These findings have broad applications beyond this specific region and can contribute to promoting sustainable land use practices and conservation efforts in other ecosystems facing similar challenges. [ABSTRACT FROM AUTHOR]

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In Brazil, forest inventory variation is calculated independently, and model-related uncertainty is systematically ignored. Although methods of estimation evaluation and sampling uncertainty together are unknown in the country, they are indispensable for improving the results of forest inventories since obtaining high quality estimates is extremely important for the country, due to its large forest areas and distinguished for being a world leader in the supply of timber and non-timber forest products, and a reference as a provider of environmental services. In view of the above, this paper presents the following study hypothesis: "Considering the uncertainty associated with inventory components: i - sampling and ii - regression model combined in the total variance of the forest inventory results in more accurate volume and biomass estimates" Thus, the aim of this study was to evaluate the uncertainties associated with sampling and the linear regression model in volume and biomass estimates in Acacia mearnsii stands in Brazil. To jointly evaluate these two sources of uncertainty, the hybrid variance estimator was used, with an analytical approach. The results showed that if model uncertainty is not considered, the total uncertainty is underestimated by 6.51%. In biomass estimates, the total uncertainty is underestimated by 18.74%. Ignoring uncertainty in total estimates can lead to uninformed decisions in forest management, with economic implications, particularly in biomass estimates, where the associated variation is even greater than in volume due to the nature of this variable. [ABSTRACT FROM AUTHOR]

Current techniques of forest inventory rely on manual measurements and are slow and labor intensive. Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor costs. We developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem straightness. Time use, mean absolute error (MAE), and root mean squared error (RMSE) metrics were used to compare the system against manual methods, and to compare the system against itself (reproducibility). Depth-derived diameter measurements of the stems at the heights of 0.3, 1.4, and 2.7 m achieved RMSE of 1.7, 1.5, and 2.7 cm, respectively. The ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained foresters. The ForSense system was also consistent, achieving sub-centimeter diameter difference with subsequent measures and less than 4% difference in straightness value between runs. This method of forest inventory, which is based on depth-image computer vision, is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion (SFM) photogrammetry and ground-based LiDAR or terrestrial laser scanning (TLS). [ABSTRACT FROM AUTHOR]

Forecasts of the forest ecosystem dynamics are important for environmental protection and forest resource management. Such forecasts can support decisions about where and how to restore damaged forests and plan felling, and in forest conservation. Forest landscape models (FLM) are used to predict changes in forests at the landscape level. FLM initialization usually requires detailed tree species and age data; so, in the absence of forest inventory data, it is extremely difficult to collect initial data for FLM. In our study, we propose a method for combining data from open sources, including remote sensing data, to solve the problem of the lack of initial data and describe initializing the LANDIS-II model. We collected land cover classification and above-ground biomass products, climate, soil, and elevation data to create initial vegetation and ecoregion maps. Our method is based on some simplifications of the study object—some tree species are replaced by groups of species; the forest stand is considered homogeneous. After initialization, the natural dynamics without harvesting and disturbances were simulated by the Biomass Succession extension for 200 years. The study presents a detailed methodology that can be used to initialize other study areas and other FLMs with a lack of field data. [ABSTRACT FROM AUTHOR]

Understanding the driving mechanisms behind existing patterns of vegetation hydraulic traits and community trait diversity is critical for advancing predictions of the terrestrial carbon cycle because hydraulic traits affect both ecosystem and Earth system responses to changing water availability. Here, we leverage an extensive trait database and a long-term continental forest plot network to map changes in community trait distributions and quantify "trait velocities" (the rate of change in community-weighted traits) for different regions and different forest types across the United States from 2000 to the present. We show that diversity in hydraulic traits and photosynthetic characteristics is more related to local water availability than overall species diversity. Finally, we find evidence for coordinated shifts toward communities with more drought-tolerant traits driven by tree mortality, but the magnitude of responses differs depending on forest type. The hydraulic trait distribution maps provide a publicly available platform to fundamentally advance understanding of community trait change in response to climate change and predictive abilities of mechanistic vegetation models.

Aim: We may be able to buffer biodiversity against the effects of ongoing climate change by prioritizing the protection of habitat with diverse physical features (high geodiversity) associated with ecological and evolutionary mechanisms that maintain high biodiversity. Nonetheless, the relationships between biodiversity and habitat vary with spatial and biological context. In this study, we compare how well habitat geodiversity (spatial variation in abiotic processes and features) and climate explain biodiversity patterns of birds and trees. We also evaluate the consistency of biodiversity–geodiversity relationships across ecoregions. Location: Contiguous USA. Time period: 2007–2016. Taxa studied: Birds and trees. Methods: We quantified geodiversity with remotely sensed data and generated biodiversity maps from the Forest Inventory and Analysis and Breeding Bird Survey datasets. We fitted multivariate regressions to alpha, beta and gamma diversity, accounting for spatial autocorrelation among Nature Conservancy ecoregions and relationships among taxonomic, phylogenetic and functional biodiversity. We fitted models including climate alone (temperature and precipitation), geodiversity alone (topography, soil and geology) and climate plus geodiversity. Results: A combination of geodiversity and climate predictor variables fitted most forms of bird and tree biodiversity with

In this study, single and double entry over- and under-bark tree volume equations have been developed for Kazdağı Fir (Abies nordmanniana subsp. equi-trojani) stands within the boundaries of Safranbolu Forest Enterprise. The over- and under-bark volume values of 153 sample trees taken from different stand structures were used as the study material. Ten tree volume equations, five single- and five double-entry, were developed for over- and under-bark tree volume estimations. Four goodnes-of-fit criteria, including adjusted coefficient of determination, bias, mean absolute error, and root mean square error, were used to determine the most successful models. The suitability of the equations developed for the region was checked with an independent data set as well Wilcoxon test, and it was concluded that the equations were suitable for the study area (p>0.05). All tree volume equations proposed for the region had a coefficient of determination value over 0.95. In addition, the mean bias of the single- and double-entry models found to be the most successful for the region were calculated as -0.0211 m3 and -0.0122 m3 for the over-bark volume estimations, and -0.0201 m3 and 0.0045 m3 for the under-bark volume estimations, respectively. Single- and double-entry tree volume tables were prepared for over- and under-bark volume estimations by using the successful equations obtained as a result of the study.

Abstract Background Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is “invisible” to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data. Results ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km2) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha−1 yr−1. The biomass carbon change was 0.405 (0.13) Mg ha−1 yr−1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha−1 yr−1, and the change in SO carbon was − 0.01 (0.003) Mg ha−1 yr−1. Conclusions Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach.

Climate change is being intensified by anthropogenic emission of greenhouse gasses, highlighting the value of forests for carbon dioxide storing carbon in their biomass. Seasonally dry tropical forests are a neglected, threatened, but potentially critical biome for helping mitigate climate change. In South America, knowing the amount and distribution of carbon in Caatinga seasonally dry vegetation is essential to understand its contribution to the global carbon cycle and subsequently design a strategic plan for its conservation. The present study aimed to model and map the spatial distribution of the potential forest biomass stock across 32 forest fragments of Caatinga, in the state of Bahia, Brazil, using regression kriging and Inverse Square of Distance techniques, building from point measurements of vegetation biomass made on-the-ground in ecological plots. First, a model for estimating biomass was fitted as a function of environmental variables to apply regression kriging, and then applied to the maps of the selected components. Elevation, temperature, and precipitation explained 46% of the biomass variations in the Caatinga. The model residuals showed strong spatial dependence and were mapped based on geostatistical criteria, selecting the spherical semivariogram model for interpolation by ordinary kriging. Biomass was also mapped by the Inverse Square of Distance approach. The quality of the regression model suggests that there is good potential for estimating biomass here from environmental variables. The regression kriging showed greater detail in the spatial distribution and revealed a spatial trend of increasing biomass from the north to south of the domain. Additional studies with greater sampling intensity and the use of other explanatory variables are suggested to improve the model, as well as to maximize the technique’s ability to capture the actual biomass behavior in this newly studied seasonally dry ecosystem.

Forest production and growth are obtained from statistical models that allow the generation of information at the tree or forest stand level. Although the use of regression models is common in forest measurement, there is a constant search for estimation procedures that provide greater accuracy. Recently, machine learning techniques have been used with satisfactory performance in measuring forests. However, methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS) and Random Forest are relatively poorly studied for predicting the volume of wood in eucalyptus plantations in Brazil. Therefore, it is essential to check whether these techniques can provide gains in terms of accuracy. Thus, this study aimed to evaluate the use of Random Forest and ANFIS techniques in the prognosis of forest production. The data used come from continuous forest inventories carried out in stands of eucalyptus clones. The data were divided into 70% for training and 30% for validation. The algorithms used to generate rules in ANFIS were Subtractive Clustering and Fuzzy-C-Means. Besides, training was done with the hybrid algorithm (descending gradient and least squares) with the number of seasons ranging from 1 to 20. Several RFs were trained, varying the number of trees from 50 to 850 and the number of observations by five leaves to 35. Artificial neural networks and decision trees were also trained to compare the feasibility of the techniques. The evaluation of the estimates generated by the techniques for training and validation was calculated based on the following statistics: correlation coefficient (r), relative Bias (RB), and the relative root mean square error (RRMSE) in percentage. In general, the techniques studied in this work showed excellent performance for the training and validation data set with RRMSE values 0.98. The RF presented inferior statistics about the ANFIS for the prognosis of forest production. The Subtractive Clustering (SC) and Fuzzy-C-Means (FCM) algorithms provide accurate baseline and volume projection estimates; both techniques are good alternatives for selecting variables used in modeling forest production.

Gardens play a key role in the definition of the cultural landscape since they reflect the culture, identity, and history of a people. They also contribute to the ecological balance of the city. Despite the fact that gardens have an historic and social value, they are not protected as much as the rest of the existing heritage, such as architecture and archaeological sites. While methods of built-heritage mapping and monitoring are increasing and constantly improving to reduce built-heritage loss and the severe impact of natural disasters, the documentation and survey techniques for gardens are often antiquated. In addition, inventories are typically made by non-updated/updateable reports, and they are rarely in digital format or in 3D. This paper presents the results of a comprehensive study on the latest technology for laser scanning in gardens. We compared static terrestrial laser scanning and mobile laser scanning point clouds generated by the Focus 3D S120 and the Leica BLK2GO, respectively, to evaluate their quality for documentation, estimate tree attributes, and terrain morphology. The evaluation is based on visual observation, C2C comparisons, and terrain information extraction capabilities, i.e., M3C2 comparisons for topography, DTM generation, and contour lines. Both methods produced useful outcomes for the scope of the research within their limitations. Terrestrial laser scanning is still the method that offers accurate point clouds with a higher point density and less noise. However, the more recent mobile laser scanning is able to survey in less time, significantly reducing the costs for site activities, data post-production, and registration. Both methods have their own restrictions that are amplified by site features, mainly the lack of plans for the geometric alignment of scans and the simultaneous location and mapping (SLAM) process. We offer a critical description of the issues related to the functionality of the two sensors, such as the operative range limit, light dependency, scanning time, point cloud completeness and size, and noise level.

Abstract We compared sampling techniques in a managed native forest in Paragominas, Pará, Brazil. Our goal in this study was to evaluate the feasibility of using Double Sampling (DS) and Sampling with Partial Replacement (SPR), when compared to Continuous Forest Inventory (CFI), to estimate the wood stock for trees with DBH ≥ 20 cm in a managed forest. In our results, DS had the best volume prediction, generating a sampling error of 5.20% (11.48 m³ ha-1) on the second occasion 3.86% (8.78 m³ ha-1). The average volume increment, estimated for the forest in the monitored period (2014-2016) was 6.88 m³ ha-1, with a relative sampling error of 63.09%. Therefore, as an alternative and of low cost, we suggest using DS in successive forest inventories in monitoring areas of forest resources in the Brazilian Amazon.

Recently, the development of geospatial information acquisition technology has been accelerating. Interest in this technology as a way to solve the problem of labor shortages in the forest sector is increasing. In this study, a forest survey was conducted by acquiring data on trees using a laser scanner and detecting individual trees through processing and analysis. Data on the study site were acquired using terrestrial and drone-mounted laser scanners, and a georeferencing point cloud was effectively created through data processing. Diameter at breast height, tree height, crown, and crown-based height were effectively measured by removing the ground from the point cloud data generated in the forest area and classifying individual trees. In addition, we suggest that water pipe measurement is possible through the combination of drone-mounted laser scanner and terrestrial laser scanner data. The existing volume calculation method using a timber volume table and the method using a point cloud were compared. For this comparison, we propose a method of calculating the timber volume using the point cloud. [ABSTRACT FROM AUTHOR]

For more than three decades, major efforts in sampling and analyzing tree diversity in South America have focused almost exclusively on trees with stems of at least 10 and 2.5 cm diameter, showing highest species diversity in the wetter western and northern Amazon forests. By contrast, little attention has been paid to patterns and drivers of diversity in the largest canopy and emergent trees, which is surprising given these have dominant ecological functions. Here, we use a machine learning approach to quantify the importance of environmental factors and apply it to generate spatial predictions of the species diversity of all trees (dbh ≥ 10 cm) and for very large trees (dbh ≥ 70 cm) using data from 243 forest plots (108,450 trees and 2832 species) distributed across different forest types and biogeographic regions of the Brazilian Amazon. The diversity of large trees and of all trees was significantly associated with three environmental factors, but in contrasting ways across regions and forest types. Environmental variables associated with disturbances, for example, the lightning flash rate and wind speed, as well as the fraction of photosynthetically active radiation, tend to govern the diversity of large trees. Upland rainforests in the Guiana Shield and Roraima regions had a high diversity of large trees. By contrast, variables associated with resources tend to govern tree diversity in general. Places such as the province of Imeri and the northern portion of the province of Madeira stand out for their high diversity of species in general. Climatic and topographic stability and functional adaptation mechanisms promote ideal conditions for species diversity. Finally, we mapped general patterns of tree species diversity in the Brazilian Amazon, which differ substantially depending on size class. [ABSTRACT FROM AUTHOR]

Forest tree biomass estimation is a significant issue for the forest management system and to mitigate climate change. Aboveground biomass (AGB) is an indicator of the potential productivity of the ecosystem. However, an accurate estimation method of carbon stock is important for perfect carbon accounting. So, selecting a more appropriate method for biomass assessment is crucial to reaching the goal. Though allometric equations have been used to estimate AGB for both individual species as well as multiple species for regional and pan-tropical scales, species-specific models for a smaller area produce better accuracy. The current initiative has been taken to develop individual tree-level species-specific and generalized local allometric models in a non-destructive manner for the Tripura state of Northeast India, which was the main limitation of carbon stock estimation in the region. Five different forms of log-transformed linearized power equations were tested in the regression models, with AGB as the response variable. The best fit models were chosen based on adjusted R2, F-statistic, Akaike information criterion, Breusch-Pagan test, Variance Inflation Factor, and three assumptions of linearity. A paired t test was conducted between the predicted and observed AGB values. The best fit generalized equation developed in this study was found to be AGB = 1.03 × exp (− 3.95 + 1.09 ln D2 + 0.97 ln H − 0.20 N). Compared with some existing pan-tropical and regional models for their predictive accuracy, this equation expressed the highest R2 value of 0.9089 in an independent testing dataset. When measuring the tree height is difficult due to a closed canopy, the equation AGB = 1.07 × exp (− 2.77 + 2.55 ln D) can be applied to obtain a quick estimation using just the DBH. The equations presented here may be the instrument for estimating carbon stock at the local level. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system's capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management. [ABSTRACT FROM AUTHOR]

Forest productivity is a key driver of forest growth and yield and a critical information need for forest management and planning. Traditionally, this information has come from field plots, but these are expensive to measure and have limited coverage. Remote sensing, on the other hand, can provide forest inventory attributes on landscape scales and with a relatively low cost. A common predictor of forest productivity is site index (SI), traditionally estimated from age and height. In plantations, age can often be treated as a known quantity, but in natural-origin forests (of which Canada has vast swaths), age is often unknown and must be estimated, requiring expensive field work and resulting in a high level of error which, in turn, introduces error into SI estimates. The objective of this study is to generate estimates of SI from two successive LiDAR captures. The 99th percentiles (p99) of LiDAR returns from two successive captures 13 years apart were used along with species-specific SI curves to estimate SI. The results were compared to field-based estimates of SI for two major boreal species, jack pine and black spruce in managed and unmanaged conditions. Overall, the difference between the LiDAR-based SI and the field estimate was 2% with a relative mean squared error of 18%. For the few situations in which the height change was small or negative (less than 0.5%/year), SI was estimated from the average p99 and an assumed age of 100. The advantage of this method is that it does not require field sampling or estimates of age. Using two successive LiDAR captures, wall to wall estimates of SI can be generated at the grid cell level (e.g., 20 × 20 m), a level of detail generally not found in inventories. Overall, our results demonstrate the excellent potential for estimating SI from LiDAR alone, without age, to provide detailed productivity information for forest management and inventory that has been lacking in most large-scale inventories until now. [ABSTRACT FROM AUTHOR]

Stem frequency distributions provide useful information for pre-harvest planning. We compared four inventory approaches for imputing stem frequency distributions using harvester data as reference data and predictor variables computed from airborne laser scanner (ALS) data. We imputed distributions and stand mean values of stem diameter, tree height, volume, and sawn wood volume using the k-nearest neighbor technique. We compared the inventory approaches: (1) individual tree crown (ITC), semi-ITC, area-based (ABA) and enhanced ABA (EABA). We assessed the accuracies of imputed distributions using a variant of the Reynold’s error index, obtaining the best mean accuracies of 0.13, 0.13, 0.10 and 0.10 for distributions of stem diameter, tree height, volume and sawn wood volume, respectively. Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. The forest attribute, inventory approach, stand size and the laser pulse density had significant effects on the accuracies of imputed frequency distributions, however the ALS delay and percentage of deciduous trees did not. This study highlights the utility of harvester and ALS data for imputing stem frequency distributions in pre-harvest inventories.

Background: Measuring stem diameter (SD) is a crucial foundation for forest resource management, but current methods require expert personnel and are time-consuming and costly. In this study, we proposed a novel device and method for automatic SD measurement using an image sensor and a laser module. Firstly, the laser module generated a spot on the tree stem that could be used as reference information for measuring SD. Secondly, an end-to-end model was performed to identify the trunk contour in the panchromatic image from the image sensor. Finally, SD was calculated from the linear relationship between the trunk contour and the spot diameter in pixels. Results: We conducted SD measurements in three natural scenarios with different land cover types: transitional woodland/shrub, mixed forest, and green urban area. The SD values varied from 2.00 cm to 89.00 cm across these scenarios. Compared with the field tape measurements, the SD data measured by our method showed high consistency in different natural scenarios. The absolute mean error was 0.36 cm and the root mean square error was 0.45 cm. Our integrated device is low cost, portable, and without the assistance of a tripod. Compared to most studies, our method demonstrated better versatility and exhibited higher performance. Conclusion: Our method achieved the automatic, efficient and accurate measurement of SD in natural scenarios. In the future, the device will be further explored to be integrated into autonomous mobile robots for more scenarios. [ABSTRACT FROM AUTHOR]

Ecologists are increasingly relying on national forest inventories to address a wide variety of issues. The 'FIESTA' R package (Forest Inventory ESTimation and Analysis) is a tool that enables customized investigations using the extensive sample-based inventory data collected across all lands in the US by the US Dept of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program. To date, the complex nature of the FIA inventory constrains many users to conduct only limited analyses through existing tools with pre-specified geographic boundaries, timeframes, and auxiliary data under a single statistical estimation process. Yet, the rapid evolution of available remotely sensed data and statistical methods present the opportunity to conduct spatial and temporal analyses of forest attributes that are much more relevant to many pressing ecological, environmental, economic, and social issues in the US, The 'FIESTA' package was developed to augment the current set of available tools by providing a flexible platform that accommodates evolving technologies and leading-edge estimation techniques. The package contains a collection of functions that can query FIA databases, summarize sample-based inventory data, extract and aggregate auxiliary spatial data, and generate estimates with associated variances. [ABSTRACT FROM AUTHOR]

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks. [ABSTRACT FROM AUTHOR]

Forest age is one of the most important ecosystem characters for accurately estimating the magnitude and potential of carbon sink in forest ecosystems. During the past 40 years, national ecological restoration projects have led to the near doubling of the forest cover area in China, which has also substantially affected the dynamics of forest age. Therefore, there is an urgent need to generate long‐term forest age maps for China. This study reconstructed China forest age datasets (CFAD) from 1980 to 2015 at five year intervals at a 1 km spatial resolution by merging a satellite‐based forest age map in 2010 and forest cover dynamic maps from 1980 to 2015. The random forest method was used to reconstruct the forest age where forest age could not be inferred from the forest age base map in 2010 directly. CFAD showed a good agreement with the province‐level mean forest age derived from the several national forest inventories (R2 ranged from 0.66 to 0.86). In general, the younger forests are mainly distributed in southern and eastern China. The older forests are mainly distributed in the mountain areas of northeast, northwest and southwest China. The average age of China's forests increased from 18.2 to 44.0 years old from 1980 to 2015. Based on the current forest age and future afforestation planning, the average forest age in China is predicted to reach 71.6 years old in 2060. The CFAD provides an alternative data set to obtain improved estimates of local and national forest carbon sinks in China. Plain Language Summary: Planting trees and protecting trees from timber harvest will help China to achieve the goal of carbon neutrality by 2060. The large scale national ecological restoration projects from the 1980s have substantially changed the structure of forest age in China. However, accurate long‐term forest age datasets for China are still unavailable. Therefore, we produced a new forest age data set for China from 1980 to 2015 by combining a reliable satellite‐based forest age base map in 2010 and high quality forest cover dynamic maps from 1980 to 2015. Using the latest four national forest inventories data set as independent observations, we found the new forest age map data set had a high validation accuracy. High quality and long‐term forest age maps are essential to better estimate the carbon dioxide absorption potential of trees and design of optimal forest management policies. Key Points: We reconstructed long‐term China forest age datasets by combining forest inventories, satellite‐based forest age and forest cover datasetsThe 1 km resolution forest age maps showed a good agreement with the provincial mean forest age derived from the national forest inventoryThe spatial and temporal patterns of changes in China's forest age are illustrated [ABSTRACT FROM AUTHOR]

Forests provide a wide range of ecosystem services and information requirements on forests have grown considerably. Nevertheless, collecting information in the forest is expensive and for this reason assessment of forest resources strongly relies on statistical sampling. However, plot measurement remains essential even when remote-sensing data are used, and field assessments are still among the costliest components of forest inventories. Studies on the costs for plots survey are limited and usually based on expert evaluation rather than on data. This article analysed the relationship between the time needed for measuring forest plots and their site-related characteristics (slope, terrain roughness), stand features (number of trees, subsample trees, stumps, coarse woody debris, understorey vegetation) and protocol-related procedures, by means of univariate and multivariate analyses. Analyses showed that the time needed for measuring plots depends on the workload or the intensity of fieldwork. Especially, the number of variables surveyed matters, because the variables explained the measuring time variation by an additive effect, suggesting that within a complex field protocol the total number of measurements taken may not represent properly the overall intensity of work. Marking in an effective way permanent plots is recommendable because the retrieval success of the pins buried into the ground was the most important explanatory variable. Presence of understorey vegetation was more important than the number of individuals measured. The results obtained are consistent and logical, but the variance explained was limited, suggesting that predictability of the measuring time under complex field protocols might be intrinsically limited by the interactions among factors with opposite effects and especially by the adaptation of the surveyors to specific circumstances. For example, effects of physical tiredness were not detected in the days when two plots were measured; conversely, measuring a second plot reduced the measuring time of the first, an event most likely dependent on the surveyors’ behaviour. Under the conclusion that predictability is low by nature, the inference of studies based on simulation data and simplified protocols to practical applications was finally discussed. [ABSTRACT FROM AUTHOR]

Brazil is one of the world's wood short-fiber producers, cultivating 7.5 million hectares of eucalypt trees. Foresters and resource managers often face difficulties in surveying reliable Eucalyptus productivity levels for the purpose of purchasing and prospecting lands. Spatial data science (DS) and machine learning (ML) provide powerful approaches to make the best use of the large datasets available today. Agriculture has made great use of these approaches, and in this paper, we explore how forestry can benefit as well. We hypothesized that both DS and ML techniques can be used to improve Eucalyptus productivity zoning based on multiple operational datasets of tree growth and environment. Based on more than 12,000 permanent forest inventory plots of commercial Eucalyptus plantations and the climate, soil, and altitude variables associated with them, a supervised ML approach was adjusted to model the forest plantation productivity. A multi-tuning of the decision-tree (DT) algorithm hyperparameters was prepared to yield 450 DT models, with a better one delivering an RMSE of 53.5 m3 ha−1, split in 35 terminal nodes, here interpreted as Eucalyptus productivity zones. The DT model showed an optimum performance index of 0.83, a coefficient of determination of 0.91, a root mean squared error of 12.3 m3 ha−1, and a mean absolute percentage error only of 3.1% in predicting the testing dataset throughout the study area. The DT rule set was interpreted in a user-friendly table and was prepared to classify any location within the study area in each one of the 35 productivity zones based on the required environment variables of the DT algorithm. The high quality of the model obtained made it possible to spatialize the DT rules, providing a reliable cartographic visualization of the probability levels of true Eucalyptus productivity for a huge region of forest-based industries in Brazil. These data-science techniques also provided a yield gap analysis using a very down-to-earth approach. We estimated a yield gap by an amount of 4.2 × 107 m3, representing a few more than 113,000 ha, or 15% of the current forest base. This is the amount of avoided area expansion to accumulate the same wood stock in case the productivity is raised to the attainable level in each zone. This present study provided deeper analysis and reproducible tools to manage forest assets sustainably. [ABSTRACT FROM AUTHOR]

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Individual tree detection using airborne laser scanning (ALS) can provide relevant data to complement forest inventory data. Local Maxima-based (LM) methods for individual tree detection are suitable for applications over large extents, but their performance depends on the type of pre-processing of the input data, as well as forest structure and composition. We developed a model that improves LM through statistical modeling using prior knowledge about forest structure. The model selects the optimal canopy height model (CHM) pre-processing filters based on forest structure variables like the dominant canopy height and degree of cover derived from ALS data, the dominant leaf type derived from Sentinel data, and terrain metrics. The model performance was evaluated by assessing tree detection errors for the canopy stem count in National Forest Inventory (NFI) plots in Switzerland (n=5254). For plots with point densities of more than 15 points per square meter and, at most, 6 years between ALS acquisition and inventory (n=2676), the results showed a mean absolute error of 61 stems per ha compared to 174 stems per ha when detecting trees using an unprocessed CHM. The model showed a stable performance for different dominant leaf types (broadleaved-dominated, mixed, coniferous-dominated) and for different degrees of cover. We consider the developed model to be suitable for applications that require data on forest structure or individual tree positions and heights over large areas.

In forest inventories, field data are needed for the prediction of tree volumes. However, gathering field data requires resources, such as labour, equipment, and data management operations. This means that time and budget, as well as quality, must be carefully considered when National Forest Inventory (NFI) field measurement activities are planned. Therefore, the development of cost efficient, simple, safe and reliable measurement methods and tools are of great interest. To date, upper stem diameter (d6), which provides a more reliable estimation of tree stem volume, has typically been measured with a parabolic calliper. In this study, the performance of the Criterion laser-based dendrometer was examined for d6 measurements. A total of 326 sample trees were measured multiple times with three different measurement instruments. These instruments were used to measure diameter at breast height (dbh) as well as d6 measurements. Bland-Altman plots and measurement error variances were used to determine measurement instrument reliability. For all trees, the standard deviation for the laser based dendrometer was 18.73 mm at dbh and 15.36 mm for the d6 measurements. When the performance of Criterion was analysed with reference to the mean value of repeated measurements, the standard deviation in the dbh measurements was 12.21 mm, and 8.88 mm in the d6 measurements.

Aim: The ongoing climate warming is expected to reshuffle understorey plant community composition by increasing the occurrence of warm‐adapted species at the expense of cold‐adapted species. This process has been evidenced before by a warming community temperature index (CTI) over time. However, data indicate that the local tree canopy can partly explain an observed lag between understorey plant CTI and climate warming rates, though landscape‐scale forest cover effects have not yet been investigated. Here, we test the hypothesis that the amount of forest cover in the landscape lowers local CTI. Location: Temperate forests in France. Time period: 2005–2019. Major taxa studied: Forest vascular plants. Methods: We compared 2,012 pairs of neighbouring French forest inventory plots with contrasting percentages of forest cover within a 1‐km radius area (landscape forest cover). We computed the difference in the CTI of the understorey communities for each pair and tested the contributions of the landscape‐scale forest cover, local canopy cover, and soil conditions to the differences in CTI. Results: Plots located in highly forested areas (> 80% in the 1‐km area) had an average CTI 0.26 °C lower (0.81 °C SD) than plots in sparsely forested areas (< 30% in the 1‐km area). Fifty percent of this difference was explained by landscape‐scale forest cover. Bioindicated soil conditions such as pH and available nutrients, which correlated with cold‐adapted species preferences, explained the remaining 50%. Main conclusions: Highly forested landscapes allow cold‐adapted species to survive in given macroclimatic conditions. These landscapes meet cold‐adapted species' soil requirements and may cool the regional climate. Further microclimatic studies are needed to confirm the cooling capacity of landscape‐scale forest cover. [ABSTRACT FROM AUTHOR]

Forest inventory has been relying on labor-intensive manual measurements. Using remote sensing modalities for forest inventory has gained increasing attention in the last few decades. However, tools for deriving accurate tree-level metrics are limited. This paper investigates the feasibility of using LiDAR units onboard uncrewed aerial vehicle (UAV) and Backpack mobile mapping systems (MMSs) equipped with an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) to provide high-quality point clouds for accurate, fine-resolution forest inventory. To improve the quality of the acquired point clouds, a system-driven strategy for mounting parameters estimation and trajectory enhancement using terrain patches and tree trunks is proposed. By minimizing observed discrepancies among conjugate features captured at different timestamps from multiple tracks by single/multiple systems, while considering the absolute and relative positional/rotational information provided by the GNSS/INS trajectory, system calibration parameters and trajectory information can be refined. Furthermore, some forest inventory metrics, such as tree trunk radius and orientation, are derived in the process. To evaluate the performance of the proposed strategy, three UAV and two Backpack datasets covering young and mature plantations were used in this study. Through sequential system calibration and trajectory enhancement, the spatial accuracy of the UAV point clouds improved from 20 cm to 5 cm. For the Backpack datasets, when the initial trajectory was of reasonable quality, conducting trajectory enhancement significantly improved the relative alignment of the point cloud from 30 cm to 3 cm, and an absolute accuracy at the 10 cm level can be achieved. For a lower-quality trajectory, the initial 1 m misalignment of the Backpack point cloud was reduced to 6 cm through trajectory enhancement. However, to derive products with accurate absolute accuracy, UAV point cloud is required as a reference in the trajectory enhancement process of the Backpack dataset. [ABSTRACT FROM AUTHOR]

Background: Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is "invisible" to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data. Results: ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km2) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha−1 yr−1. The biomass carbon change was 0.405 (0.13) Mg ha−1 yr−1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha−1 yr−1, and the change in SO carbon was − 0.01 (0.003) Mg ha−1 yr−1. Conclusions: Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach. [ABSTRACT FROM AUTHOR]

Automated forest machines are becoming important due to human operators' complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps. [ABSTRACT FROM AUTHOR]

Deep learning, neural networks and other data-driven processing techniques are increasingly used in the analysis of LiDAR point cloud data in forest environments due to the benefits offered in accuracy and adaptability to new environments. One of the downsides of these techniques in practical applications is the requirement for manually annotated data necessary for training neural networks, which can be time consuming and costly to attain. We develop an approach to training neural networks for forest tree stem segmentation from point clouds that uses synthetic data from a custom tree simulator, which can generate large quantities of training examples without manual human effort. Our tree simulator captures the geometric characteristics of tree stems and foliage, from which automatically-labelled synthetic point clouds can be generated for training a semantic segmentation algorithm based on the PointNet++ architecture. Using evaluations on real aerial and terrestrial LiDAR point clouds from a range of different forest sites, we demonstrate our synthetic data-trained models can out-perform, or provide comparable performance with models trained on real data from other sites or when available real training data is limited (increases in IoU from 1–7%). Our simulation code is open-source and made available to the research community. [ABSTRACT FROM AUTHOR]

The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall‐to‐wall spatial predictions of both attributes in 1‐km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r2 =.35 [.12,.51] (mean [min, max]) than for tree density r2 =.23 [.05,.42]. The best predictive performance when mapping tree height was for broadleaf and coniferous‐broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision‐making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets. [ABSTRACT FROM AUTHOR]

Field-based forest inventory plots are fundamental for many forest studies. These on-the-ground measurements of small samples of forested areas provide foresters with key information such as the size, abundance, health, and value of their forests. Recently, forest inventory plots have begun to be used as ground validation for tree features automatically extracted from remotely sensed data sets. Additionally, machine learning methods for feature extraction rely heavily on large quantities of training data and require these field forest inventory measurement datasets for algorithm training. Undermining the usefulness of forest inventory plot data as validation or training data is the positional uncertainty of plot location measurements. Because global navigation satellite systems (GNSS) cannot reliably measure plot center coordinates under thick tree canopy, plot center coordinates usually contain multiple meters of horizontal error. We present a method for reliably measuring plot center coordinates in which plot centers are individually marked with low-cost targets, allowing plot centers to be manually measured from orthoimagery captured during the leaf-off season. Our plot center measurements are shown to have less than 10 cm of horizontal error, an improvement of an order of magnitude over traditional GNSS methods. [ABSTRACT FROM AUTHOR]

Biopower, electricity generated from biomass, is a major source of renewable energy in the US. About ten percent of US non-hydro renewable electricity in 2020 was generated from biomass. Despite significant growth in woody biomass use for electricity in recent decades, a systematic assessment of associated impacts on forest resources is lacking. This study assessed associations between biopower generation, and selected timberland structure indicators and carbon stocks across 438 areas surrounding wood-using and coal-burning power plants in the Eastern US from 2005 to 2017. Timberland areas around plants generating biopower were associated with more live and standing-dead trees, and carbon in their respective stocks, than comparable areas of neighboring plants only burning coal. We also detected an inverse association between the number of biopower plants and number of live and dead trees, and respective carbon stocks. We discerned an upward temporal trajectory in carbon stocks within live trees with continued biopower generation. We found no significant differences related to the amount of MWh biopower generation within the analysis areas. Net impacts of biopower descriptors on timberland attributes point to a positive trend in selected ecological conditions and carbon balances. The upward temporal trend in carbon stocks with longer generation of wood-based biopower may point to a plausibly sustainable contribution to the decarbonization of the US electricity sector. [ABSTRACT FROM AUTHOR]

The direct effects of relative humidity and soil on aboveground biomass (AGB) versus the indirect effects mediated by stand structural diversity remain unclear in forest ecosystems across large-scale elevation gradients. Forest inventory data containing 15,260 individual trees and 104 tree species from 200 forest plots were collected. The result shows that the relative humidity, elevation, and Coefficient of Variation of Diameter at breast height (CVD) significantly influence AGB in the Tropical Thorn Forest (TTF). Regarding elevation, CVD was positive and significant, and relative humidity and SR negatively impacted AGB in sub-tropical broad-leaved forests (STBLF). In moist temperate mixed forests (MTMF), soil phosphorus and CVD have a significant positive impact, while relative humidity, elevation, and SR negatively influence AGB. Elevation and CVD have positive, while SR and soil phosphorus have a negative and insignificant effect on AGB in Dry Temperate Conifer Forests (DTCF). Soil phosphorus and relative humidity positively affected AGB (β = 0.021), while elevation, CVD, and SR negatively affect AGB in dry temperate, pure pine forests (DTPPF). Relative humidity and soil phosphorus have a positive direct effect on AGB in multi-species forests. The current study suggests that AGB primarily depends on relative humidity, soil phosphorus, and elevation in different forest types. [ABSTRACT FROM AUTHOR]

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research. [ABSTRACT FROM AUTHOR]

If available forest inventory techniques or measurement instruments are not satisfying or appropriate for monitoring a particulare forest, options include developing a new inventory technique and/or modifying an available instrument. In this study, we present a new application of horizontal line sampling (HLS) in combination with a crown relascope as a potential alternative to line intersect sampling (LIS) to monitor coppice forests (CF). In CFs, the stem basal area relascope is not applicable, since trees often lack distinct stems and are not visible at breast height. Our study was conducted in an open oak-dominated CF in western Iran and we focussed on two forest attributes: above-ground biomass (AGB) and the total number of trees (N). We assessed the field performance of HLS and LIS in CFs and evaluate various line transect lengths and crown relascope angles. Our results indicate that longer line transects (80 m) produce smaller relative sampling errors compared to shorter ones (20 m) and smaller relascope angles (36.8°) are more precise than larger ones (67.4°). In terms of cost-efficiency, HLS is superior or equivalent to LIS in some cases. While HLS and LIS gave different estimates of AGB and N, the difference was not statistically significant at the 95% level. Both relascope angle and line transect length impact the efficiency of HLS. Our study provides evidence that this technique can be useful for monitoring forest conditions. [ABSTRACT FROM AUTHOR]