National Forest Inventory Research Articles

Use of national forest inventory data to develop stand density driven models for understorey shrubs and overstorey fuel variables and associated temporal dynamics in commercial plantations

Biomass tends to accumulate both in overstorey and understorey layers of forests growing in productive areas. The rapid growth of forest plantations established in productive areas can thus also lead to the creation of fire-prone landscapes. In this study, we assessed the extent to which overstorey stand density determines current values of the fine shrub fuel load (Wshr_G1) in the understorey and also two major canopy fuel characteristics related to crown fire activity -canopy base height (CBH) and canopy bulk density (CBD)- and the temporal dynamics of these. For this purpose, we developed two types of models for the three fuel complex variables: i) extreme response models, to define the upper limit of Wshr_G1 and CBD and the lower limit of CBH, the values of which may depend on stand density; and ii) dynamic models, to estimate the probability of increasing fuel loads and rate of change when a more hazardous situation is predicted. Data were obtained from 8087 plots, measured in the third, fourth and fifth Spanish National Forest Inventories, in plantations dominated by the major commercial species in northern Spain (Pinus pinaster, Pinus radiata, Pinus sylvestris and Eucalyptus globulus). The extreme response models generally explain more than 60% of the observed variability in the three fuel complex variables. According to these models, maximum shrub fuel load and CBD and minimum CBH in stands of the four tree species were limited by stand basal area, with different responses by different species. The dynamics of the fuel complex variables were determined by canopy cover, estimated as a proxy for stand density. The use of a two-step regression approach enabled realistic modelling of the dynamics of the fuel complex variables, allowing for a decrease in the shrub fuel biomass and in CBD between two time points. The dynamic models developed are age-independent and are therefore useful for practical applications because they can be applied to any stand without the need to know the stand age. Overall, the study findings provide insights into the complex relationship between both understorey and overstorey fuels and stand density in commercial plantations. As assessment of forest fuel complex variables is a prerequisite for most fire management activities, the study findings have important implications for forest and fuel management and planning in the region.

Accuracy of the Copernicus High-Resolution Layer Forest Type (HRL FTY) assessed with domestic NFI sampling plots in Poland

Abstract Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018. The study compares stratified field-based data from the National Forest Inventory (NFI) with remote sensing data on forest variables, at the pixel level, in order to identify suitable methods for accuracy and area uncertainty estimation. Additionally, the paper introduces and presents a variety of accuracy metrics applicable to assess overall uncertainties in GHG inventories. The results indicate that the High-Resolution Layer Forest Type (HRL FTY) product (part of the broader Copernicus Land Monitoring Service [CLMS] portfolio), assessed using NFI field-based information, achieved an overall accuracy (OA) of 69.2%. This metric varies among particular nature protection forms, with the highest observed ones in Natura 2000 sites of 70.45%. The primary source of map errors was associated with distinguishing between broad-leaved and coniferous forest areas. Improving future maps necessitates more precise differentiation between species to better support national forest monitoring systems for the purpose of greenhouse gas (GHG) inventories where information on the spatial distribution and variability of forests sources, biodiversity assessment, threat prevention, estimation of carbon content is becoming an important part of the associated reporting system.

Effect of labour costs on wood harvesting costs and timber provision

Efficient forest operations are essential for forest enterprises, who provide wood and numerous ecosystem services for the society. Important factors influencing the efficiency of forest operations, and thus the harvesting costs, are the level of mechanization, the harvesting method applied, the forest road network, and the training of the labourers. The cost of labour, i.e. salaries, is another important driver of harvesting costs. However, its effect on and relative importance for overall harvesting costs is poorly described in the scientific literature. Thus, this study aims to analyse the influence of labour costs in more detail, especially on the country-wide wood harvesting potentials. In addition, we aimed to cross-compare the effect of labour costs with the effect of switching to a more efficient harvesting method. For this purpose, we calculated timber harvesting costs with varying salaries for all sample plots of the Swiss National Forest Inventory (NFI) for both, the currently applied harvesting method recorded in the NFI, and the potential best suitable harvesting method. A 1% change in labour costs affects harvesting costs by 0.33–0.77%, depending on the harvesting method applied. The influence is larger for systems that involve a large share of motor-manual work and for cable-based methods. Changing labour costs by ± 30% affects the number of plots for which timber harvesting is economically feasible, by 5 to 15 percent points. The effect of switching from the current to the best suitable harvesting method is comparable to that of reducing labour costs by 15–30%. These results indicate that the efficiency of wood harvesting can be increased with further mechanization and does not require cutting salaries of forestry personnel. In that, our results may inform forestry planning and policy making at regional to national level.

On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.

Large in-stream wood yield during an extreme flood (Storm Alex, October 2020, Roya Valley, France): Estimating the supply, transport, and deposition using GIS

During major floods, rivers erode their banks and thus recruit large wood pieces from the riparian zones. There is still a lack of knowledge about the transport of large wood, the volumes involved and the flux distribution, i.e. the large wood connectivity at catchment scale. During storm Alex (October 2020), the French Roya catchment (394 km2) experienced a paroxysmal morphogenic flood involving massive bank erosion. The riparian vegetation was largely recruited, with large wood contributing to logjams and bridge destruction. This paper presents a methodology for volumetric assessment of the large wood fluxes involved. Simple approaches are used to (i) quantify the inputs from stand density data from the national forest inventory and from source areas based on diachronic analysis of active channels highlighting the erosion of 87 ha of wooded areas; and (ii) quantify the volumes deposited via an exhaustive manual digitisation of 16,846 pieces of large wood deposited on 59 km of channels on the Roya and its tributaries. This catchment-scale, large wood connectivity analysis shows that the flood recruited and transported downstream a volume of around 14,000 m3 of large wood (uncertainty range: 7000–29,500 m3). Drone observations of the Roya River mouth in Italy and satellite images showing a raft of driftwood, several km long, drifting off the Roya River mouth in the aftermath of the flood corroborate our findings.

Effect of Logging on the Structure, Diversity, and Tree Species Composition of the Forests of Papua New Guinea

The forests of Papua New Guinea (PNG) play a fundamental role in sustaining the livelihood of local communities and provide significant income to the country derived from the export of timber. After land conversion for agriculture, logging has been identified as the second most common disturbance factor. Conservation activities must be based on a better understanding of the impact of logging. This study analyzes the differences in terms of structure, diversity, and tree species composition between forest types and disturbance regimes in PNG forests. Data were collected from 117 circular plots surveyed according to the protocol of the National Forest Inventory. Sites were situated in unlogged and logged lowland (99 plots) and montane forests (18 plots). The survey recorded 4,558 tree specimens belonging to 258 families, 323 genera, and 789 species. Although data showed the need for a further sampling effort, some general patterns emerged. Montane forest is clearly distinct in terms of structure, diversity, and species composition. Unlogged and logged lowland forests differed only in terms of structure and species composition, but not according to diversity indexes. Thus, logging activities are having an impact mainly on lowland and most accessible areas. Our study established a baseline related to the tree species component of PNG forests. Together with other taxonomic groups, it will be used to monitor the implementation of initiatives aimed at promoting the reduction of emissions from deforestation and forest degradation (REDD+), which can offer significant economic, environmental, and social benefits.

Estimation of plant density based on presence/absence data using hybrid inference

Monitoring of plant populations has become more and more important, especially in the current context of environmental change. In this paper, we propose methods to estimate plant density from presence/absence surveys, wherein the presence or absence of each species is recorded on sample plots. Presence/absence sampling is a useful and relatively simple method for monitoring state and change of plant communities. Moreover, it has advantages compared to traditional plant cover assessment, the latter being more prone to observer bias. We present a hybrid estimation framework, that combines model- and design-based inference features, in which a generalised linear model (for binary presence/absence data) and an inhomogeneous Poisson model (for plant locations) are used to estimate plant density in a region of interest. We look at two different cases, the first one with a known area and the second one where the area is unknown and must be estimated. Our methods are applied to real data on Vaccinium vitis-idaea from the Swedish National Forest Inventory as well as simulated data to assess the performance of our estimators of plant density and corresponding variance estimators. The results obtained are promising and indicate that this method has a potential to add considerable analytic strength to monitoring programmes that collect presence/absence data.

Harmonized decay classification for dead wood in Nordic national forest inventories

ABSTRACT Dead wood quality is typically recorded as a biodiversity indicator and as part of forest carbon estimates, using decay classification systems. In large-scale national forest inventories (NFIs), these systems typically differ among countries. Harmonizing them would allow analyzes over broader scales and the use of larger data sets. Here, we exemplified the development of a harmonized decay classification for the NFIs of Finland, Norway, and Sweden, using wood density as an objective harmonizing criterion. We sampled 441 Scots pine, Norway spruce, birch and trembling aspen in different conditions and decay classes, measured their density and developed the harmonized classification for each species and dead wood type (standing and fallen). The assignments relied on minimizing within-class variance in wood density in the harmonized classes. Assigned into three (standing dead wood) and four (fallen dead wood) classes, the harmonization led to modest losses of information, especially in the advanced decay stages. How the original decay classes were assigned into the harmonized classes differed slightly, depending on the tree species and type. This is an advantage over the expert assessment that typically treat all species and types of dead wood the same way despite clear differences in decomposition pathways.

Environment-induced growth changes in forests of Finland revisited - a follow-up using an extended data set from the 1960s to the 2020s

After a rising trend for 1971 – 2013, during which the annual volume growth of the forests of Finland increased by more than 70 %, a recent reduction has been observed. We analyzed the development of annual growth in the forest of Finland, focusing on the component not explainable by changes in growing stock. The data originate from nine consecutive Finnish National Forest Inventories. In the data, diameter increments were measured from increment cores and tree height increments from standing sample trees in the field. We developed models predicting periodic (5 years) annual volume increment per hectare with properties of the trees and stands as predictor variables. Deviations from model-predicted values were interpreted to be induced by environmental variation. The development was analyzed for all tree species combined and separately for three species groups: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and broadleaves. We observed a rising growth trend not solely explainable by increased growing stock. The species groups produced rather a similar pattern in different parts of Finland: from the 1960s to the mid-1990s, the observed volume increment was mainly below the model-predicted level, thereafter above it. During the current century, the difference between observed and predicted annual volume increment has shown a downward trend for Scots pine. For Norway spruce, the difference has continued to increase in southern Finland, but shows little change in the north. For broadleaved species, the difference between measured and predicted increment shows a recent increase as well, though not as large as for Norway spruce. The geographical pattern of the environment-induced increment component was described in more detail via maps using a 75 km × 75 km grid. The changing environment has enhanced forest productivity in Finland over a period of nearly six decades, but recent years have not been favorable for Scots pine, which represents 50 % of the growing stock volume of the forests of Finland.

Country-wide assessment of biodiversity, naturalness and old-growth status using national forest inventory data

Assessing forest biodiversity, naturalness and old-growth status (B-N-OG) is crucial for supporting sustainable forest planning, yet comprehensive monitoring networks specifically designed for such purposes are lacking in many countries. National Forest Inventories (NFIs) are the official source of statistics on status and trends of forests. While initially designed for wood production assessment, NFI data may be pivotal for ecological forest monitoring, thanks to their robust sampling protocols—enabling statistical inference—and regular field campaigns that ensure continuous information updates. As a consequence, in this study we explore the potential of NFIs for estimating B-N-OG indexes, aiming to establish compatible, scientifically relevant, and cost-effective indicators using existing NFI data at a European level. Based on data from the 2005 Italian NFI collected from 6563 plots, 18 indicators were selected following previous experiences and then used to estimate B-N-OG aggregated indexes. Relationships between the 18 indicators and the three indexes were investigated, along with comparisons of their relationships relative to forest type categories, management types and protected versus non-protected areas. Results confirm that NFI data offer valuable insights into specific B-N-OG indexes, especially concerning forest structure and deadwood. Moreover, the indexes contribute to developing meaningful relationships across geographic regions, forest categories and types of management. However, limitations in NFI field protocols are evident, as they are not explicitly designed for certain indicators. The study suggests the potential for NFIs to evolve into accessible, harmonized European reference networks for B-N-OG assessment for better supporting sustainable forest management, planning and conservation decisions related to forest ecosystems.

The relationship between forest structure and naturalness in the Finnish national forest inventory

Abstract There is considerable interest in identifying and locating natural forests as accurately as possible, because they are deemed essential in preventing biodiversity loss. In the boreal region, natural forests contain a substantial amount of dead wood and exhibit considerable variation in tree age, size, and species composition. However, it is difficult to define natural forests in a quantitative manner. This is an issue, for example, in the Finnish national forest inventory. If naturalness could be related to the metrics derived from tree measurements, it would be easier to locate natural forests based on the inventory data. In this study, we investigated the value of metrics computed from tree locations and tree sizes for the characterization of a key aspect of naturalness, namely, structural naturalness as defined in the Finnish national forest inventory. We used L-moments, Gini coefficient, Lorenz asymmetry, and interquartile range to quantify the variations in tree size at the plot level. We summarized the spatial pattern of trees with a spatial aggregation index. We compared the structural metrics, species proportions, and stand age using the classes of structural naturalness described in the Finnish national forest inventory, which have been determined in the field without strict numerical rules. These categories are ‘natural’, ‘near-natural’, and ‘non-natural’. We found that the forests evaluated as structurally natural had larger variations in tree size and species composition and showed a more clustered spatial pattern of trees on average, although the variation in the structural metrics was considerable in all three classes. In addition, we used the structural metrics to predict naturalness by employing a random forest algorithm. Based on the structural metrics, it was possible to obtain high precision in the classification only if we simultaneously accepted low recall, and vice versa; the link between the inspected metrics and naturalness evaluated in the field was weak. The stand age separated the three classes more clearly and it also improved the classification.

Harmonization and variation of deadwood density and carbon concentration in different stages of decay of the most important Central European tree species

Coarse woody debris (CWD) is a major component of the ecosystem carbon (C) balance. The estimation of C storage in CWD is an important element of the German greenhouse gas (GHG) reporting of forests, which is mainly based on the German National Forest Inventory. The deadwood C stock is calculated based on deadwood volume and, according to deadwood density (DD) and carbon concentration (CC) for each decay class (DC). Yet, the data basis of DD and CC per DC for above-ground CWD is still insufficient since there are very few country-specific measurements. Values from literature provide a first approximation for national-level estimates. However, different DC systems often prevent the use of DD and CC of other countries. Therefore, we developed a conversion method for harmonization of these data with the German four-class system. Following this, we conducted a meta-analysis to calculate mean DD and CC values for the main Central European tree species and to assess their variation. Significantly lower DDs were observed with increasing DC, except for beech between DC 3 and 4. Compared to spruce and pine, DD of beech CWD was significantly higher, overall as well as in DC 1 and 2. Species became similar in DD in advanced decay stages. A maximum of 92% of the variation in DD could be explained mainly by DC, CWD type, tree species and their interaction. DD values were mostly higher than current values in GHG reporting. CC increased with increasing DC in spruce and pine and was higher than in beech CWD, where no variation was detected. About 86% of the variation in CC could be explained mainly by DC, tree species and their interactive effect. The default value of 50% employed by the Intergovernmental Panel on Climate Change might under- (spruce, pine) and/or overestimate (spruce, pine, beech) the real CC depending on DC by up to 3.4 (pine) and/or 4.2% (beech). Based on our calculated mean DD and CC values, the accuracy of C stock assessment in deadwood as part of the GHG reporting for Germany can be substantially improved.

FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach

Abstract. The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha−1) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha−1, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an R2 of 0.63 with an MAE of 30 m3 ha−1. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from https://doi.org/10.5281/zenodo.7840108 (Schwartz et al., 2023).

A Transition-Matrix Growth Model and Equilibrium Curve for Natural Forests in Jiangxi Province

Researchers build growth models to predict the growth of forest stands and propose management measures to improve the overall quality of these stands. In this study, data collected from 91 sample plots from the eighth (2010) and ninth (2015) Chinese National Forest Inventories in Jiangxi Province were used to establish a transition-matrix growth model. Then, 12 potential equilibrium curves were set to guide forest management, and a transition-matrix growth model was used to predict stand growth in Jiangxi Province. In each 10-year management period, trees with diameters that exceeded the equilibrium curve were cut down. The results show that species diversity (H1), size diversity (H2), and basal area (B) have statistically significant influences on growth, mortality, and recruitment. Moreover, the high accuracy of the transition-matrix growth model is demonstrated. According to the simulation results, B = 35 m2/ha, the maximum diameter of retained trees Dmax = 45 cm and the adjacent diameter ratio q = 1.7 constitute the optimal equilibrium curve to guide forest management. The diameter distribution guided by the equilibrium curve is reverse J-shaped and is associated with significant increases in the hardwood stock volume and current annual growth. Under the guidance of the equilibrium curve, the forests in Jiangxi Province can be reasonably managed, produce more high-economic-value timber, and achieve a more stable species composition. This study will help maximize the ecological and economic benefits of forests and provide a reference for the realization of the sustainable development of forestry. Furthermore, the results can be used to improve the facility and accuracy of natural forest harvesting.

Estimating changes of forest carbon storage in China for 70 years (1949–2018)

In the realm of forest resource inventory and monitoring, stand-level biomass carbon models are especially crucial. In China, their importance is underscored as they form the bedrock for estimating national and international forest carbon storage. This study, based on the data from 52,700 permanent plots in the 9th National Forest Inventory (NFI) of China, was directed towards developing these models. After computing biomass and carbon storage per hectare using specific tree models for 34 species groups, we devised robust volume-derived biomass and carbon storage models for 20 forest types. The application of these models and historical data reveals notably a decline in China's forest carbon storage to 4.90Pg by the late 1970s due to aggressive forest exploitation. However, subsequent conservation and afforestation campaigns have affected a recovery, culminating in a storage of 8.69Pg by the 9th NFI. Over the past 40 years, China's forest carbon storage has surged by 3.79Pg, split between natural forests (2.25Pg) and planted forests (1.54Pg). In benchmarking against three pre-existing models, we discerned discernible biases, underscoring the need for larger modeling sample sizes. Overall, our models stand as a monumental stride in accurately gauging forest carbon storage fluctuations in China, both regionally and nationally.

Economically optimised target state of uneven-aged forest management for main forest types in Slovakia

Abstract The study presented a bioeconomic modelling approach for an uneven-aged mixed forest management planning. Regression models for transition (increment), and ingrowth (regeneration) used the National Forest Inventory (NFI) of Slovakia and regional inventory data. Mortality was based on salvage logging records. Models were specific for five tree species within three forest types (FT) (oak with hornbeam and beech, beech, mixed fir-beech-spruce). Net timber prices were calculated with regard to stem quality. Tree growth depended on crown characteristics. The regression models were adjusted to three main geobiotope (GBT) sites per FT. Forest growth was simulated with the density-dependent stand-level matrix transition model. Financial optimisation of harvest was sensitive to an interest rate. Long-time optimisation stabilised in a steady state equilibrium characterised by a stable diameter distribution. Target diameters were specific for site and tree species, and were highest for fir, a dense crown, a good stem quality, and a lower interest rate. Standing timber volume varied from 150 m3 ha–1 (oak forests, 2% interest rate) to 400 m3 ha–1 (beech and fir-beech-spruce forests, 0.5% interest rate). Harvested volume varied from 38 to 93 m3 ha–1 per 10 years, stand basal area (ba) varied from 19 to 36 m2 ha–1 depending on the site, timber price, and interest rate. The discussion pointed out that the relative low volume of the oak FT resulted from the light-demanding characteristics of oak. The mean of oak mosaic structures was lower compared to the high level of more storeys present in the single tree selection structures in beech and mixed fir-beech-spruce forests.

Quantifying old-growth forest of United States Forest Service public lands

Old-growth forests are globally valued for their ecological attributes, cultural significance, and in many cases their rarity. Yet, defining and quantifying these forests has been a difficult task. This study developed an approach to consistently estimate extent of old-growth forest on United States Department of Agriculture (USDA) Forest Service National Forest System (NFS) lands, using NFS regional old-growth definitions applied to the US national forest inventory (conducted by the USDA Forest Service Forest Inventory and Analysis [FIA] program). This method was developed in response to a presidential order (EO#14072, April 22, 2022) and federal laws (e.g., Infrastructure Investment and Jobs Act, 2021; Inflation Reduction Act, 2022). We worked with NFS experts to obtain regionally approved criteria for establishing old growth status based on NFS definitions, assessments, and related documents. NFS regional old growth definitions focus on structural characteristics of forests with criteria for old growth status commonly including minimum abundance of large live trees (in eight of nine regions), tree or stand age (in eight of nine regions), and dead large tree density (in three of nine regions). Determining the regional criteria to use was straightforward for some NFS regions where old-growth forest definitions were specific, and in some cases, had already been applied to FIA data to quantify old-growth forest area. In other NFS regions, such as where definitions have never been applied in an operational manner, or where there were merely assessments of remnant old-growth forest conditions, determining exact criteria was more difficult. We estimate that there are approximately 10 million ha of old growth across NFS forests, as defined by NFS criteria, with the preponderance in the western US states. This study produces the first old-growth forest assessment at the national scale based on NFS definitions and FIA’s statistically-rigorous national forest inventory of the US. These methods can be repeated with future inventories or modified when definitions change to produce updated estimates of old-growth forest attributes, and such work is already underway.

Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory

Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even-aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper-parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 – 2.11 m, and the relative RMSE was less than 10 % (7.68 – 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden.

Mangrove biomass and carbon estimates for REDD+ from national forest inventory in two regions of Myanmar

Myanmar has the second largest area of mangrove forests in South East Asia while suffering high deforestation rates and forest degradation. However, monitoring results from existing studies in mangrove forests vary widely while specific estimates on carbon density for this forest type are deficient. Also, data from mangrove inventory plots used for the 2018 Myanmar forest reference level refer only to commercial timber and do not include other carbon pools. The results in this study based on four carbon pools allow for updating the emission factors in mangrove forests expressed in CO2 from 125.43 ± 15.06 Mg ha –1 to 1 377.07 ± 67.04 Mg ha –1 for Ayeyarwady region (Pyapon, Pathein, Latputta districts) and 1 583.27± 75.18 Mg ha –1 for Tanintharyi region (Kawthaung, Dawei districts) and can be used in (sub)national or jurisdictional forest reference level for REDD+. In addition, the georeferenced biomass estimates are useful for the calibration of space-based models of biomass estimation.

Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 m2. Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.