Forest Inventory Research Articles

Landscape structures and stand attributes jointly regulate forest productivity

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.

FastFuels: Advancing wildland fire modeling with high-resolution 3D fuel data and data assimilation

Acquiring detailed 3D fuel data for advanced fire models remains challenging, particularly at large scales. To address this need, we present FastFuels, a novel platform designed to generate detailed 3D fuel data and accelerate the use of advanced fire models. FastFuels integrates existing fuel and spatial data with innovative modeling techniques to represent complex 3D fuel arrangements across landscapes. It leverages data sources including the Forest Inventory and Analysis (FIA) database and plot imputation maps, and incorporates advanced features such as data assimilation from LiDAR. This research demonstrates FastFuels’ capabilities through two applications: evaluating fuel treatment effectiveness with the Fire Dynamics Simulator and simulating a prescribed fire operation using QUIC-Fire. FastFuels provides previously unavailable 3D fuel data at landscape scales, empowering informed decision-making, detailed investigations of fuel treatment impacts, and higher-resolution risk assessments. Its flexible data assimilation and model-agnostic outputs accelerate advanced fire science and support fire management decisions.

Allometric Models of Aboveground Biomass in Mangroves Compared with Those of the Climate Action Reserve Standard Applied in the Carbon Market

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.

Hindcasting and updating Landsat-based forest structure mapping across years to support forest management and planning

Forest vegetation mapping that integrates forest inventory data with multispectral remote sensing data provides valuable geospatial products for public land management agencies, but resource managers may require rapid updating of maps as new imagery becomes available (updating) or retrospective mapping for times prior to forest inventory plot measurement (hindcasting). While forest attribute mapping using Landsat multispectral imagery is common, the accuracy of applying models outside of reference epoch to support long-term forest monitoring is not normally quantified. We examine whether a Landsat-based mapping approach can support robust, temporally consistent multivariate mapping of forest structure and composition data in support of forest management planning and landscape analysis. Specifically, we ask: how accurate forest attribute mapping was when hindcasting or updating outside of a period of time when forest inventory plot data were available (reference epoch)? In the western Cascade Mountains of Oregon and California, USA, we used the gradient nearest neighbor approach to annually impute USDA Forest Inventory and Analysis (FIA) plot data (2001–2016) to all 30-m forested pixels based on temporally smoothed Landsat multispectral imagery (1986–2021), including basal area, canopy cover, quadratic mean diameter of dominant trees, stand height, and the density of large diameter trees. We made extrapolations from models fit to a 10-year reference epoch to both earlier periods (2001–2006 hindcast) and to later period (2011–2016 update) and quantified prediction accuracies relative to models based on the full data (2001–2016). To evaluate the influence of spatial scale on hindcasting and updating, we compared full and extrapolation model predictions at pixel-level (0.09 ha) and hexagon-level (780 ha).At the plot-level, we found no strong differences between the full and extrapolation model predictions for R2 and mean error nor among predicted vs. observed regression coefficients. At the pixel-level, average differences due to hindcasting and updating were near zero, though differences varied up to 20 % across pixels. At the hexagon-level, the range in map differences was small (+/- 5 %), but hindcasting resulted in lesser forest attribute predictions. We observed greater variability in pixel-level and hexagon-level prediction differences when hindcasting or updating was temporally further away from the reference period. Using 2001 hindcast and 2016 updated maps as a case study, we found that with hindcasting and updating map differences were spatially aggregated across the study region. Our results support Landsat-based hindcasting and updating of forest attribute mapping beyond the time period covered by forest plot data. Our results suggest aggregating data to coarse spatial resolutions may minimize differences due to hindcasting and updating. Further research is needed to identify the key drivers for prediction differences to improve the accuracy of both hindcasting and updating as a basis for forest monitoring.

Band configurations and seasonality influence the predictions of common boreal tree species using UAS image data

Key messageData 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.ContextThe effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration.AimsThe 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.MethodsTree 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.ResultsPrediction 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.ConclusionThe 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.

Modelling and Mapping of Aboveground Carbon of Oluwa Forest Reserve Using LandSat 8 TM and Forest Inventory Data

This research was carried out within the Oluwa Forest Reserve to evaluate and forecast its capacity for aboveground carbon sequestration using data from the Landsat Thematic Mapper. The Oluwa Forest Reserve, situated in Ondo State, Nigeria, is renowned for its abundant biodiversity and vast expanse. Assessing the forest's aboveground biomass and carbon traditionally involves intricate and expensive processes necessitating the expertise of diverse professionals and specialized equipment. Hence, this study investigated the utilization of Geographic Information System (GIS) and Remote Sensing (RS) technology, employing Landsat bands to calculate spectral indices and construct linear models for predicting the aboveground carbon sequestration potential of the tropical rainforest ecosystem within the Oluwa Forest Reserve. The measured aboveground carbon from sample plots, alongside the estimated spectral indices, was utilized to simulate the distribution of aboveground carbon across the Oluwa Forest Reserve. A positive linear correlation was identified between the observed data and the estimated spectral indices. Consequently, linear models were developed, and the most suitable model was determined through statistical analysis. The average aboveground carbon estimated from the sample plots was 150.70 tons per hectare (t/ha), closely aligning with the predicted value of 149.80 t/ha. Statistical analysis yielded a coefficient of determination of 94% and a Root Mean Square Error of 6.38E-16. These results indicate that the selected model accurately predicts the distribution of aboveground carbon within the Oluwa Forest Reserve. This study underscores the importance of spectral data, GIS, and RS in the efficient modelling and mapping of aboveground carbon in extensive forest ecosystems.

Mobile laser scanning as reference for estimation of stem attributes from airborne laser scanning

The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose a model-based forest inventory method that uses reference data collected by a car-mounted mobile laser scanning (MLS) system along boreal forest roads. This approach is used for the estimation of diameter at breast height (DBH) and stem volume at the individual tree-level from airborne laser scanning (ALS) data. In addition, we compare the estimates obtained using the proposed method with the ones derived from reference data collected by traditional field inventory of 265 field plots systematically distributed over the study area. The accuracy of the estimates remained comparable regardless of the reference dataset used for estimation of DBH and stem volume. When using the field inventory dataset for model training, the root mean square error (RMSE) of DBH estimates were 4.06 cm (18.8 %) for Norway spruce trees, 6.3 cm (29.6 %) for Scots pine and 8.61 cm (55.9 %) for deciduous trees. Similarly, when evaluating predictions based on the MLS dataset as reference, RMSEs were equal to 3.97 cm (18.4 %) for Norway spruce, 6.12 cm (28.8 %) for Scots pine, and 8.98 cm (58.3 %) for deciduous trees. In general, biases were below 1 cm for most species classes, with the exception of deciduous trees. The accuracy of stem volume also had RMSEs varying across different tree species. For the estimates based on traditional field inventory, the RMSEs were 0.176 m3 (38.8 %) for Norway spruce, 0.228 m3 (52.4 %) for Scots pine and 0.246 m3 (158 %) for deciduous trees. When using the MLS dataset as a reference, the RMSEs were equal to 0.176 m3 (38.8 %), 0.228 m3 (52.4 %), and 0.246 m3 (158 %) for Norway spruce, Scots pine, and deciduous trees, respectively. Car-mounted MLS demonstrated its potential as an efficient alternative for collecting reference data in remote sensing-based forest inventories, which could complement traditional methods.

Assessing Larix principis-rupprechtii productivity and its determinants based on national forest inventory data in Hebei Province, China

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.

Modelling and mapping the abundance of lingonberry (Vaccinium vitis-idaea L.) in Norway

Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests.

Zero-inflated multivariate tobit regression modeling

A frequent challenge encountered in real-world applications is data having a high proportion of zeros. Focusing on ecological abundance data, much attention has been given to zero-inflated count data. Models for non-negative continuous abundance data with an excess of zeros are rarely discussed. Work presented here considers the creation of a point mass at zero through a left-censoring approach or through a hurdle approach. We incorporate both mechanisms to capture the analog of zero-inflation for count data. Additionally, primary attention has been given to univariate zero-inflated modeling (e.g., single species), whereas data often arise jointly (e.g., a collection of species). With multivariate abundance data, a key issue is to capture dependence among the species at a site, both in terms of positive abundance as well as absence. Therefore, our contribution is a model for multivariate zero-inflated continuous data that are non-negative. Working in a Bayesian framework, we discuss the issue of separating the two sources of zeros and offer model comparison metrics for multivariate zero-inflated data. In an application, we model the total biomass for five tree species obtained from plots established in the Forest Inventory Analysis database in the Northeast region of the United States.

Tree growth history, legacy of large-scale forest management and climatic variability in North Finland

Tree growth history was investigated in North Finland to study the recently observed reduction in Scots pine growth. Tree-ring records from the National Forest Inventory (NFI) were compared to changes that have occurred in forest management activities and to variations in the North Atlantic Oscillation (NAO), as similar comparisons have not been previously made with NFI data. A phase of increased growth was dated to the early 1970s when the forest management activities were intensified on a large scale. More recently, growth has notably declined since the early 2000s which coincides with reduced activity to clean the ditches to ensure they are draining the substrate adequately. The growth reduction appeared greater for trees from stands with higher basal area. Trees representing sites where the first cleaning of the stand (thinning) had been carried out did not show reduced growth. NAO indices correlated positively with growth, especially during the cold season. An interpretation on the value of snow conditions in a dynamic interaction with management history/stand density is put forth to explain the growth decline observed in NFI data over the past two decades.

A Climate-Sensitive Mixed-Effects Individual Tree Mortality Model for Masson Pine in Hunan Province, South–Central China

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate.

Modeling and spatialization of biomass and carbon stock using unmanned Aerial Vehicle Lidar (Lidar-UAV) metrics and forest inventory in cork oak forest of Maamora

Recently, the concerns about climate change have heightened the need for effective methods for estimating and mapping Biomass and Carbon stock at local, national, continental, and global scales. Reliable Biomass and Carbon stock quantification and spatialization is a challenge, especially in degraded Mediterranean Cork oak forest. To estimate and map Biomass (Btree−Total) and Carbon stock (Cst−total), we explored an improved approach using extracted metrics collected by Lidar-UAV (unmanned aerial vehicles Lidar), combined with forest inventory data. We approach three types of models for data analysis: Simple linear regression, multiple linear regressions, and stepwise multiple linear regression. The best Biomass and Carbon stock model fit is the Stepwise multiple linear regressions, involving the following metrics: maximum elevation, canopy cover and point cloud density and intensity. Our finding provides a quantification and spatialization Biomass and Carbon stock model based on Lidar-UAV metrics in Cork Oak Mediterranen forest and the results confirm the degraded state of Maamora Forest with a Biomass and Carbon stock relatively medium to low.

THE IMPORTANCE OF TAXONOMIC CLASSIFICATION OF ‘PASHACO’ AND ‘HUAYRURO’ IN THE FABACEAE FAMILY FOR TIMBER PRODUCTION IN LORETO, PERU

In Peru, forest inventories traditionally group different timber species under the same commercial name. The names "pashaco" and "huayruro" in the Amazon exemplify this practice, representing the grouping and commercialization of various Fabaceae species, a family of significant forest and commercial importance in Loreto. This study aimed to taxonomically identify the "pashaco" and "huayruro" individuals a timber company recorded in its forest census and to differentiate each group's species through dendrological characterization. The company's census identified 429 "pashaco" individuals as Albizia niopioides and 150 "huayruro" individuals as Ormosia amazonica. Botanical identification of samples from 80 individuals in each group revealed that the individuals classified as A. niopioides comprised nine species: Schizolobium parahyba var. amazonicum, Dimorphandra cuprea, Pseudopiptadenia suaveolens, Hydrochorea pedicellaris, Enterolobium schomburgkii, Parkia nitida, Parkia igneiflora, Parkia decussata, and Parkia multijuga, with the last being the most common. Similarly, the "huayruro" or Ormosia amazonicaclassification included Andira macrothyrsa, Batesia floribunda, Ormosia coccinea, Ormosia amazonica, Vataireopsis surinamensis, Vatairea erythrocarpa, Hymenolobium excelsum, Hymenolobium nitidum, and Hymenolobium velutinum, with the latter being the most abundant. This study underscores the need for improved species identification in forest inventories. Dendrological characteristics of stems and leaves can aid in differentiating and identifying these species in forest censuses, fostering sustainable management practices by reducing species grouping and minimizing economic impacts in the timber forest production chain.

The Chinese Postman Problem and Simulated Annealing applied to urban forest inventory

Routing is the process of defining paths between geographically distinct points, and it has been applied in decision-making processes for services that include displacement, with potential application in the planning of urban forest inventories. Linear Programming is an operations research technique often applied to solve complex problems, such as those related to routing. Thus, the objective of this study was to verify whether the application of a routing model via Integer Linear Programming (ILP) increases the efficiency of an urban forest inventory using real data. First, we compared the empirical division used in the inventory with the application of the ILP method for routing. We also tested an approximate method (Simulated Annealing, SA) to solve the routing problem and reduce the processing time. Finally, we simulated the consequences of applying ILP in different scenarios. The results showed that the ILP improved the inventory efficiency by 6.36%, and the use of SA reduced the processing time by 43,200 times for the most complex scenario, resulting in a solution that was 1.87% of the ILP response value. Our findings show the potential application of ILP in the planning of urban forest inventories, with gains directly proportional to the number of lots in the inventory.

Eucalypt production in different rotations, sites and spatial arrangements

This work aimed to evaluate the productivity of eucalypt rotations in different site indexes and spatial arrangements. The database came from continuous forest inventories executed during the first and second rotations of 718 clonal stands (AEC 144®, AEC 224® and AEC 1528®), implanted in the 8.4 m2 plant−1 spacing and distributed in two arrangements: 3.0 × 2.8 m (rectangularity = 1.07) and 6.0 × 1.4 m (rectangularity = 4.29). Height, basal area, and volume differed between rotations as site quality improved and stands aged. In absolute terms, stands with lower site index, and installed under a larger rectangular arrangement showed less volume divergence between rotations. At 84 months of age, the first rotation of sites with the highest productive capacity tended to show 42.63 and 38.67 m3 ha−1 year−1 in smaller and larger rectangularity arrangements, respectively. The volumetric declines between the first and second rotations averaged 6.8 and 41.3 % at 84-month reference point, for arrangements of 6.0 × 1.4 m and 3.0 × 2.8 m, respectively. It is concluded that the less rectangular spatial arrangement (3.0 × 2.8 m) proved to be more productive for conducting a single rotation (high forest). Arrangement 6.0 × 1.4 m showed higher productive potential in stands managed with two rotations within the same cultivation cycle.

Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.

To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon

Tropical forests play a critical role in the global carbon cycle, storing 40–55 % of terrestrial plant carbon and significantly contributing to primary productivity. However, uncertainties persist in estimating carbon stocks and fluxes, exhibiting variation across the Neotropics, Africa, and Asia tropical forest regions. Despite hosting some of the most densely sampled forests, significant uncertainties persist in biomass and forest carbon stock estimates in the Neotropics. Although the Southwestern Amazon (SWA) forests span over 20 million hectares, no specific biomass or above- and below-ground carbon model has been calibrated for this region thus far. In our study, we conducted direct forest inventories in the SWA to address the following question: Do the allometric patterns, biomass, and carbon stocks observed in the Southwestern Amazon differ from those found in other regions of the Amazon or Pantropical? Our research reveals substantial differences in water and carbon content, biomass stocks, above- and below-ground oven-dry biomass ratios, and allometric patterns between SWA forests and other Amazonian and Pantropical forests. We have demonstrated that these differences result in overestimations of forest biomass when applying allometric equations developed for other Amazonian and Pantropical regions to the open forests of Southwestern Amazonia. This overestimation can reach up to 37 % when using equations from the eastern Amazon, and between 26 % and 46 % depending on the applied Pantropical equation. The use of an inappropriate factor for the root-to-shoot ratio in the Southwestern Amazon (SWA) can lead to overestimates of belowground oven-dry biomass by up to 20 %. To reduce uncertainties related to estimates of forest carbon stock and flux in Neotropical forests, it is necessary to enhance the density of direct biomass measurements, particularly in southwestern Amazonia.

Forestat: An R package for computing forest carbon sequestration and potential productivity

1. Forest ecosystem is a basic component of terrestrial ecosystem that mitigates the impact of climate change through absorbing significant amount of carbon dioxide. Forest is crucial in maintaining ecological equilibrium, reducing greenhouse gas emission, and preserving biodiversity. Thus, standardised and precise methodologies for measuring forest carbon sequestration and assessing its potential are of paramount importance in evaluating the impacts of climate change.2. We presented the “forestat”, an approach for assessing forest carbon sequestration and its potential based on annual growth of biomass, considering site class and forest age conditions, wrapped in an R package, which is applicable to all forest types. By using a comprehensive system of forest stand models, “forestat” contributes to a refined comprehension of relationships between forest biomass and carbon sequestration and offers an evaluation of the carbon sequestration potential in forest ecosystem, unifying the methods of carbon measurement and its potential estimation in both natural and plantation forests.3. Based on the two applications, this study (a) introduces and demonstrates the methods in R package “forestat”; (b) uses the 6th, 7th, 8th, and 9th National Forest Inventory data to investigate carbon sequestration and potential productivity of degraded forests in China; and (c) conducts suitability analysis for tree species in the Yellow River Basin.4. “forestat” provides a unified, efficient, and standardized analytical tool for forest carbon sequestration and its potential. This study also highlights the primary forest functionalities.

Drivers of snag fall rates in Fennoscandian boreal forests

Abstract 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.