National Forest Inventory Plots Research Articles

Successional dynamics of carbon sequestration in forests of the eastern United States

AbstractCarbon sequestration in the forests of the eastern United States is an important offset to the country's CO2 emissions. Much of the eastern forestland is the product of reforestation of abandoned agricultural land or recovery following clear‐cutting over a century ago. This has led to concerns that eastern forests are even‐aged and that rates of carbon sequestration will decline as forests increase in carbon. Our objective was to examine the successional dynamics of forest carbon sequestration—using live tree carbon stocks as a proxy for successional status—for the six broadly defined forest types present in the region. We used datasets from the National Forest Inventory (NFI) for the 31 US states from Minnesota south to Louisiana and eastward and analyzed live tree net carbon increment for 2007–2021, the period for which NFI plot remeasurement data were available for all 31 states. Spruce–fir and southern pines were the only forest types for which carbon increment declined even modestly over a significant fraction of the range of live tree carbon observed in the region, and southern pine–hardwood forests were the only forests in which predicted sequestration in live tree carbon declined to zero within the range of carbon stocks observed in the region. Northern hardwood–conifer forests, oak–hickory forests, and lowland forests experienced either no decline or a slight increase in sequestration in live tree carbon across the range of successional status observed in the region. Thus, the average stocks of live tree carbon per unit area increased steadily over the study period. At some point in succession, rates of mortality are expected to increase and balance gross growth, leading to zero net sequestration in live tree carbon. Mature and old‐growth stands, however, are rare in all six forest types, and mortality as a fraction of live tree carbon for all six forest types declined across the range of successional status present in the region. Our results thus provide no support for the hypothesis that the successional dynamics of forests in this region can be expected to lead to near‐term declines in net carbon sequestration.

National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data

Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation and the assessment of forest ecosystem services. In many countries, forest attributes are monitored regularly through sample-based forest inventories. In combination with satellite imagery, data from such forest inventories have a great potential for developing large-area tree species maps. Here, the high temporal resolution of Sentinel-1 and Sentinel-2 has been useful for extracting vegetation phenology, information that may also be valuable for improving forest tree species mapping.The objective of this study was to map the main tree species in Germany using combined Sentinel-1 and Sentinel-2 time series, and to identify and address challenges related to the use of National Forest Inventory (NFI) data in remote sensing applications. We generated cloud free time series with 5-day intervals from Sentinel-2 imagery and combine those with monthly Sentinel-1 backscatter composites. Further, we incorporate information on topography, meteorology, and climate to account for environmental gradients. To use NFI data for training machine learning models, we address the following challenges: 1) link satellite pixels with variable radius NFI plots, for which the precise area is unknown, and 2) efficiently utilize mixed-species NFI plots for model training and validation. In the past, accuracies for pixel-level species maps were often estimated solely for homogeneous pure-species stands. In this study, we assess how well pixel-level maps generalize to mixed plot conditions.Our results show the potential of combined Sentinel-2 and Sentinel-1 time series with NFI data for tree species mapping in large, environmentally diverse landscapes. Classification accuracy in pure stands ranged between 72% and 97% (F1-score) for five dominant species, while mapping less frequent species remained challenging. When including mixed forest stands in the accuracy assessment, accuracy decreased by 4–14 percentage points for the most dominant species groups. Our study highlights the importance of including mixed-forest stands when training and validating tree species maps. Based on these results, we discuss potentials and remaining challenges for tree species mapping at the national level. Our findings allow to further improve national-level tree species mapping with medium to high resolution data and provide guidance for similar approaches in other countries where ground-based inventory data are available.

Evaluating Forest Site Quality Using the Biomass Potential Productivity Approach

Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides for predicting forest growth under multi-aged, multi-species, and multi-layered canopy conditions. We used 2222 national forest inventory plots that were measured in four consecutive periods in the Jilin Province for this purpose. We analyzed and verified the computational methods of BPP based on the BRP and evaluated its practical significance. The results showed that growth models of the stand height, stand basal area, and stand biomass of four forest types (pure larch forest, larch broadleaf mixed forest, Mongolian oak pure forest, and Mongolian oak broadleaf mixed forest) fit adequately, BPP was greater than BRP, and this difference decreased with an increasing stand age, suggesting that the potential productivity of the middle-aged and young forest was higher than that of the mature forest, although the difference is minimal. In addition, the realistic productivity of stands with better site quality was close to the potential productivity, which is consistent with the biological significance of the potential productivity of the biomass. The degree of difference between the potential productivity of the biomass and the realistic productivity of biomass also decreases with the decline in site quality, and it can be termed as the potentially improved stand biomass. The BPP model was able to perform well in both the pure and mixed forests. The BRP not only verifies the rationality of the BPP but can be also used to quantify the forest site quality, which is helpful for evaluating forest growth and informed decision making in forestry.

Analysis of Height Growth Suggests Moderate Growth of Tilia cordata and Acer platanoides at the Native Hemiboreal Stands in Latvia

In the Eastern Baltics, climatic changes are expected to alter forest composition favouring broadleaved species. The height growth of trees influences the productivity of stands and the competitiveness of species, particularly in mixed sites, thus emphasising the necessity for accurate projections. Accordingly, height models are paramount for projecting productivity and yields of stands. As tree height growth dynamics vary regionally, regional or even local models are needed. Based upon 214 National Forest Inventory plots and 510 individual canopy trees, dominant height growth for small-leaved lime (Tilia cordata Mill.) and Norway maple (Acer platanoides L.) in Latvia were analysed. Height growth was modelled using a generalised algebraic difference approach, testing several non-linear equations. The Sloboda (for lime) and Hossfeld I (for maple) models showed the best fit and were the most realistic, predicting slower initial and middle-age (maturing period) growth, yet also displayed higher asymptotes compared to Western Europe. The predicted height at the age of 80 years was 14–33 m and 13–34 m for lime and maple, accordingly. A longer establishment period and later growth culmination suggest longer rotation, highlighting the assessment of long-term risks. In this case, supplementation of the models with climatic effects appears advantageous.

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).

Development and preliminary application of a Nature Value index to identify High Nature Value forests in the Republic of Ireland

High Nature Value (HNV) forests contribute to maintaining European biodiversity and public good supply. This study aimed to a) develop an objective and quantitative Nature Value (NV) index for the identification of HNV forests in the Republic of Ireland; and b) apply and validate the index using available data from the Irish National Forest Inventory (NFI).Following recent European definitions of HNV forest, a six-step framework was adapted from literature and used for assessing forest naturalness. The reference forest (in an Irish context) and its naturalness traits were first described. Six indicators were selected to construct a NV index and three categories (low, medium and high NV) were defined based on the range of NV scores. Using data from the Irish NFI, the approach was implemented by calculating the indicators’ values and the NV score for 1,676 forest plots. The selected indicators were tested for redundancy and the NV index was validated with available floristic variables and with forest sub-type classes. A sensitivity analysis was conducted on the weighting values of the indicators.Approximately 11% of the NFI plots were categorised as HNV. There was no redundancy between the selected indicators. The NV index was significantly positively correlated with data from the floristic variables collated by the NFI. The averages of the floristic variables per NV category were significantly different. NFI plots classified as HNV had a higher percentage of natural/semi-natural forest types than medium NV and low NV plots. The sensitivity analyses showed little effect of changes to the indicators' weighting values on a) the correlation coefficients between the floristic variables’ data and the new NV scores obtained and b) on the proportion of natural/semi-natural forests in HNV plots.This work provides an approach for the development of a NV index to identify HNV forests in a European country following the naturalness concept exclusively. The selected indicators and their weighting should be tailored to each country’s particular conditions, especially due to potential differences in the reference level of naturalness of forests and differences within NFIs.

Past decade above-ground biomass change comparisons from four multi-temporal global maps

Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (ΔAGB) products has increased in recent years. Here we assessed the past decade net ΔAGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local ΔAGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels <10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall ΔAGB map cross-correlation between maps varied in the range 0.11–0.29 (r). Reported ΔAGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that ΔAGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, ΔAGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.

Stand, plot characteristics, and tree species diversity jointly dominate the recruitment biomass of subtropical forests

Forest dynamics is largely affected by the recruitment of trees. Despite several investigations on forest recruitment biomass (FRB), it is still not well explored how tree species diversity, stand characteristics, climate, and topography regulate FRB. Moreover, there have been limited studies and discussions on FRB in typical subtropical forests in karst areas. We examined the relationship between influencing factors and forest biomass from the perspective of tree recruitment using data from 2334 Chinese National Forest Inventory (CNFI) plots in Guizhou Province. The best predictors within each group were chosen using a stepwise regression model, and the effects of five categories, including tree species diversity, stand factors (e.g., stand density), climatic factors (e.g., mean annual temperature and annual precipitation), topographic factors (e.g., elevation and slope), and plot factors (e.g., canopy density and forest composition) on FRB were determined using structural equation models (SEM). Besides this, the relative importance of each variable among five categories in determining FRB was also quantified by hierarchical partitioning analysis (HPA). Our results indicated that diversity of tree species had the highest relative importance in determining FRB (28.85 %), followed by average DBH (21.06 %), canopy density (10.29 %) based on HPA. While for five categories, stand factors contributed the most to FRB (37.04 %), followed by plot factors (29.01 %), tree species diversity (28.85 %), climatic factors (3.81 %), and topographic factors (1.29 %). Moreover, SEM showed FRB was strongly directly and significantly controlled by tree species diversity (β = 0.63, P < 0.05), while stand factors had a strong direct effect (β = 0.45, P < 0.05) and indirect effect (β = -0.78, P < 0.05). We conclude that niche complementarity is the primary mechanism driving this forest's FRB. Our findings highlight the significance of tree recruitment in maintaining subtropical forests' carbon storage capacity. Furthermore, increasing tree species diversity, particularly by combining conifers and broad-leaved trees, will encourage the recruitment of young trees.

Advancing height growth models for the improved forest reproductive material of the main tree species in Latvia

The breeding of economically important forest tree species in the Baltic Sea region has contributed notably to the availability of quality wood for bioeconomy. Accordingly, the altered stand dynamics of improved trees should be identified and incorporated in growth models to accurately reflect these gains. Such advanced models can be used for assessment of different alternatives, e.g. strategies for increased carbon sequestration. We tested and modified dynamic forms of the King-Prodan height growth function based on the remeasured National Forest Inventory plots in Latvia to predict the growth of improved Scots pine, Norway spruce and silver birch forest reproductive material (FRM) categories ‘qualified’ and ‘tested’ using height measurements from progenies of 371, 390, and 690 open-pollinated families, respectively. Both categories had steeper growth trajectories at young age compared to an unmodified function. Growth of category ‘tested’ for pine and birch exceeded that of category ‘qualified’ across the modelled age range, while trajectories mainly overlapped for spruce on lower site indices. The functions with FRM category-specific multipliers more accurately reflect the actual growth of improved stands, advancing planning of timely management activities like thinning. The single model with category-specific set of multipliers may be easy applicable in practice or incorporated in growth simulators without increased complexity for end-users. However, the predictions are limited to the sites with medium and high site indices, where improved planting stock is typically used. Keywords: GADA approach, dynamic modelling, tree breeding, FRM categories

A stand level scenario model for the Norwegian forestry – a case study on forest management under climate change

Carbon sequestration and income generation are competing objectives in modern forest management. The climate commitments of many countries depend on forests as carbon sinks which must be quantified, monitored, and projected into the future. For projections we need tools to model forest development and perform scenario analyses to assess future carbon sequestration potentials under different management regimes, the expected net present value of such regimes, and possible impacts of climate change. We propose a scenario analysis software tool (GAYA 2.0) that can assist in answering these types of questions using stand level simulations, detailed carbon flow models and an optimizer. This paper has two objectives: (1) to describe GAYA 2.0, and (2) demonstrate its potential in a case study where we analyze the forest carbon balance over a region in Norway based on national forest inventory sample plots. The tool was used to map the optimality front between the carbon benefit and net present value. We observed changes in net present value for different levels of carbon benefit as well as changes in optimal management strategies. We predicted future changes in several forest carbon pools as well as albedo and illustrated the impact of gradual increase in forest productivity (i.e., due to climate warming). Having been updated and modernized from its previous version with increased attention to forest carbon and energy fluxes, GAYA 2.0 is an effective tool that offers multiple opportunities to perform various types of scenario analyses in forest management.

Detecting the presence of natural forests using airborne laser scanning data

Centuries of forest exploitation have caused significant loss of natural forests in Europe, leading to a decline in populations for many species. To prevent further loss in biodiversity, the Norwegian government has set a target of protecting 10% of the forested area. However, recent data from the National Forest Inventory (NFI) reveals that less than 2% of Norway's forested area consists of natural forests. To identify forests with high conservation value, we used vertical and horizontal variables derived from airborne laser scanning (ALS) data, along with NFI plot measurements. Our study aimed to predict the presence of natural forests across three counties in southeastern Norway, using three different definitions: pristine, near-natural, and semi-natural forests. Natural forests are scarce, and their underrepresentation in field reference data can compromise the accuracy of the predictions. To address this, we assessed the potential gain of including additional field data specifically targeting natural forests to achieve a better balance in the dataset. Additionally, we examined the impact of stratifying the data by dominant tree species on the performance of the models. Our results revealed that semi-natural forests were the most accurately predicted, followed by near-natural and pristine forests, with Matthews correlation coefficient values of 0.32, 0.24, and 0.17, respectively. Including additional field data did not improve the predictions. However, stratification by species improved the accuracy of predictions for near-natural and semi-natural forests, while reducing accuracy for pristine forests. The use of horizontal variables did not improve the predictions. Our study demonstrates the potential of ALS data in identifying forests with high conservation value. It provides a basis for further research on the use of ALS data for the detection and conservation of natural forests, offering valuable insights to guide future forest preservation efforts.

Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data

Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE = 6.0) and smallest for QMD (RE = 1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data.

Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests

ABSTRACT The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides an extraordinary opportunity to support global large-scale forest carbon mapping, but further research is needed in order to obtain wall-to-wall forest aboveground biomass (AGB) maps with this technology. The effects of vegetation structure on the performance of canopy height and AGB modeling using ICESat-2 photon-counting light detection and ranging (LiDAR) data in Mediterranean forest areas have not been previously studied in the literature. In this study, we combined recent ICESat-2 vegetation (ATL08) data, Airborne Laser Scanning (ALS)- and field-based estimates, and a multi-sensor earth observation composite for extrapolation of AGB estimates and AGB mapping. A diverse gradient of forest Mediterranean ecosystems, distributed over 19,744.15 km2 of forest area in the region of Extremadura (Spain), with different species and structural complexity forming 5 different forest types (3 Quercus spp. dominated and 2 Pinus spp. dominated forests), was used to (i) evaluate the precision of ICESat-2 canopy height estimations, (ii) develop ICESat-2-based AGB models, and (iii) generate a spatially continuous prediction of AGB by using data from the satellite missions Sentinel-1 (S1), Sentinel-2 (S2), Phased Array L-band Synthetic Aperture Radar (ALOS2/PALSAR2), and Shuttle Radar Topography Mission (SRTM). First, ALS- and ICESat-2-derived metrics that best described canopy height (p98 and rh98, respectively) were compared at the ATL08 segment level. Second, ALS-based AGB values were derived at the ATL08 segment scale. Third, ALS-based AGB estimates at the ICESat-2 segment level were used as dependent variables to fit ICESat-2-based AGB models. Fourth, a multi-sensor approach was then implemented to predict ICESat-2-derived AGB, by means of a Random Forest (RF) modeling technique, with predictors retrieved from S1, S2, ALOS2/PALSAR2, and SRTM. Finally, RF was used to generate wall-to-wall AGB maps that were compared with field-, ALS- and ICESat-2-based observations. The agreement between the ALS- and ICESat-2-derived metrics related to the canopy height distribution was higher for Pinus spp. forest than for the Quercus spp-dominated forests. The ICESat-2-based AGB models yielded model efficiency (Mef) values between 0.56 and 0.80, with a RMSE ranging from 7.76 to 17.71 Mg ha−1 and rRMSE from 19.04 to 55.21%. The multi-sensor RF models provided the following results when compared with the ICESat-2- and ALS-based AGB observations: R 2 values of 0.63 and 0.64, and RMSE values of 11.10 Mg ha−1(rRMSE = 28.15%) and 12.28 Mg ha−1 (rRMSE = 31.45%), respectively, and an approximately unbiased result (0.03 Mg ha−1 and 0.09 Mg ha−1). When applied to the field-based validation data set (4th Spanish National Forest Inventory (SNFI-4) plots = 508), the RF-derived AGB model showed a relatively lower predictive capacity (R 2 = 0.45), a higher RMSE value (25.88 Mg ha−1) and slightly biased results (−1.47 Mg ha−1), especially for larger field-derived AGB intervals. The results of this study serve to provide an initial quantitative assessment of the ICESat-2 ATL08 data for large-scale AGB estimation. The findings suggest that a multi-sensor approach may be feasible for extrapolating ICESat-2-derived AGB estimates over areas where field or ALS reference data are not available.

Non-native tree regeneration indicates regional and national risks from current invasions

Non-native (introduced, exotic, and alien) species alter forest ecosystem processes, cause landscape change, interfere with services provided by native trees, and contribute to biotic homogenization. To quantify the degree of invasion by non-native trees in the United States, we combined two broad-scale datasets: (1) tree species occurrence data from the national Forest Inventory and Analysis (FIA) plot network and (2) ecoregions characterized by relatively homogeneous environmental conditions. Using the FIA statistical design, we created an indicator of non-native tree regeneration success by estimating the proportion of small trees (seedlings and saplings) relative to all trees for non-native species in the conterminous United States and southeast Alaska, Hawaii, and Puerto Rico. Species with at least 75 percent of their stems consisting of smaller stems were classified as highly invasive while those with 60 percent to 75 percent of smaller stems were classified as moderately invasive. Above these thresholds, non-native species are of ecological concern because they are established and reproducing successfully and therefore likely to continue to spread in the future. For the conterminous United States and southeast Alaska, we identified 16 highly invasive and four moderately invasive non-native tree species. Widespread highly invasive and relatively well-established species included Ailanthus altissima, Triadica sebifera, and Ulmus pumila. The richness of highly invasive species was the highest in parts of the Midwest and Mid-Atlantic States, followed by much of the Southeast. In Hawaii, we identified seven highly invasive and three moderately invasive non-native tree species. The most widespread highly invasive and well-established tree species in the archipelago were Psidium cattleyanum, Psidium guajava, Ardisia elliptica, and Syzygium cumini. The largest numbers of highly invasive species were inventoried in the lowland/leeward dry and mesic forests of O’ahu and the lowland wet and mesic forests of Hawai’i Island. Puerto Rico had 17 highly invasive and two moderately invasive tree species. The most widespread and well-established non-native species were Leucaena leucocephala, Spathodea campanulata, Coffea arabica, Syzygium jambos, and Melicoccus bijugatus. The results of this assessment offer insights into which species are most likely to alter forest ecosystems and which forests may be effectively managed to control invasive trees.

Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products

Forest map products are widely used and have taken benefit from progresses in the multisource forest inventory approaches, which are meant to improve the precision of forest inventory estimates at high spatial resolution. However, estimating errors of pixel-wise predictions remains difficult, and reconciling statistical outcomes with map products is still an open and important question.We address this problem using an original approach relying on a model-based inference framework and k-nearest neighbours (k-NN) models to produce pixel-wise estimations and related quality assessment. Our approach takes advantage of the resampling properties of a model-based estimator and combines it with geometrical convex-hull models to measure respectively the precision and accuracy of pixel predictions. A measure of pixel reliability was obtained by combining precision and accuracy.The study was carried out over a 7,694 km2 area dominated by structurally complex broadleaved forests in centre of France. The targeted forest attributes were growing stock volume, basal area and growing stock volume increment. A total of 819 national forest inventory plots were combined with auxiliary data extracted from a forest map, Landsat 8 images, and 3D point clouds from both airborne laser scanning and digital aerial photogrammetry. k-NN models were built independently for both 3D data sources. Both selected models included 5 auxiliary variables, and were generated using 5 neighbours, and most similar neighbours distance measure. The models showed relative root mean square error ranging from 35.7% (basal area, digital aerial photogrammetry) in calibration to 63.4% (growing stock volume increment, airborne laser scanning) in the validation set. At pixel level, we found that a minimum of 86.4% of the predictions were of high precision as their bootstrapped coefficient of variation fall below calibration’s relative root mean square error. The amount of extrapolation varied from 4.3% (digital aerial photogrammetry) to 6.3% (airborne laser scanning). A relationship was found between extrapolation and k-NN distance, opening new opportunities to correct extrapolation errors. At the population level, airborne laser scanning and digital aerial photogrammetry performed similarly, offering the possibility to use digital aerial photogrammetry for monitoring purposes.The proposed method provided consistent estimates of forest attributes and maps, and also provided spatially explicit information about pixel predictions in terms of precision, accuracy and reliability. The method therefore produced high resolution outputs, significant for either decision making or forest management purposes.

Assessing the Predictive Power of Democratic Republic of Congo’s National Spaceborne Biomass Map over Independent Test Samples

Remotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas (GHG) inventory and monitoring in tropical countries. However, most countries have not used maps as the reference data for GHG inventory due to the lack of confidence in the accuracy of maps and of data to perform local validation. Here, we use the first national forest inventory (NFI) data of the Democratic Republic of Congo to perform an independent assessment of the country’s latest national spaceborne carbon stocks map. We compared plot-to-plot variations and areal estimates of forest aboveground biomass (AGB) derived from NFI data and from the map across jurisdictional and ecological domains. Across all plots, map predictions were nearly unbiased and captured c. 60% of the variation in NFI plots AGB. Map performance was not uniform along the AGB gradient, and saturated around c. 290 Mg ha−1, increasingly underestimating forest AGB above this threshold. Splitting NFI plots by land cover types, we found map predictions unbiased in the dominant terra firme Humid forest class, while plot-to-plot variations were poorly captured (R2 of c. 0.33, or c. 0.20 after excluding disturbed plots). In contrast, map predictions underestimated AGB by c. 33% in the small AGB woodland savanna class but captured a much greater share of plot-to-plot AGB variation (R2 of c. 0.41, or 0.58 after excluding disturbed plots). Areal estimates from the map and NFI data depicted a similar trend with a slightly smaller (but statistically indiscernible) mean AGB from the map across the entire study area (i.e., 252.7 vs. 280.6 Mg ha−1), owing to the underestimation of mean AGB in the woodland savanna domain (31.8 vs. 57.3 Mg ha−1), which was broadly consistent with the results obtained at the provincial level. This study provides insights and outlooks for country-wide AGB mapping efforts in the tropics and the computation of emission factors in Democratic Republic of Congo for carbon monitoring initiatives.

Plot size matters: Toward comparable species richness estimates across plot-based inventories.

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot‐based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample‐based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country‐specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan‐European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot‐based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.

A Tool for Long-Term Forest Stand Projections of Swedish Forests

The analysis of forest management strategies at landscape and regional levels forms a vital part of finding viable directions that will satisfy the many services expected of forests. This article describes the structure and content of a stand simulator, GAYA, which has been adapted to Swedish conditions. The main advantage of the GAYA implementation compared to other resources is that it generates a large number of management programmes within a limited time frame. This is valuable in cases where the management programmes appear as activities in linear programming (LP) problems. Two methods that are engaged in the projections, a climate change response function and a soil carbon model, are designed to complement other methods, offering transparency and computational effectiveness. GAYA is benchmarked against projections from the Heureka system for a large set of National Forest Inventory (NFI) plots. The long-term increment for the entire NFI set is smaller for GAYA compared with Heureka, which can be attributed to different approaches for modelling the establishment of new forests. The carbon pool belonging to living trees shows the same trend when correlated to standing volume. The soil carbon pool of GAYA increases with increased standing volume, while Heureka maintains the same amount over the 100-year projection period.

A National Multi-Scale Assessment of Regeneration Deficit as an Indicator of Potential Risk of Forest Genetic Variation Loss

Genetic diversity is essential because it provides a basis for adaptation and resilience to environmental stress and change. The fundamental importance of genetic variation is recognized by its inclusion in the Montréal Process sustainability criteria and indicators for temperate and boreal forests. The indicator that focuses on forest species at risk of losing genetic variation, however, has been difficult to address in a systematic fashion. We combined two broad-scale datasets to inform this indicator for the United States: (1) tree species occurrence data from the national Forest Inventory and Analysis (FIA) plot network and (2) climatically and edaphically defined provisional seed zones, which are proxies for among-population adaptive variation. Specifically, we calculated the estimated proportion of small trees (seedlings and saplings) relative to all trees for each species and within seed zone sub-populations, with the assumption that insufficient regeneration could lead to the loss of genetic variation. The threshold between sustainable and unsustainable proportions of small trees reflected the expectation of age–class balance at the landscape scale. We found that 46 of 280 U.S. forest tree species (16.4%) may be at risk of losing genetic variation. California and the Southeast encompassed the most at-risk species. Additionally, 39 species were potentially at risk within at least half of the seed zones in which they occurred. Seed zones in California and the Southwest had the highest proportions of tree species that may be at risk. The results could help focus conservation and management activities to prevent the loss of adaptive genetic variation within tree species.

Improving forest management by implementing best suitable timber harvesting methods

Efficient forest operations are required for the provision of biodiversity and numerous ecosystem services, such as wood production, carbon sequestration, protection against natural hazards and recreation. In numerous countries, under difficult terrain conditions, the costs of forest management and harvesting are not covered by timber revenue. One possible option to increase the cost-effectiveness of the forestry sector is the application of state-of-the-art harvesting and extraction techniques, so-called best suitable harvesting methods. We present a case study from Switzerland, where a lack of competitiveness in the forestry sector is of particular interest, with the aim of quantifying the efficiency gains if estimated best suitable harvesting methods were to be rigorously applied instead of the currently applied harvesting methods. For this purpose, we developed a spatial decision support system to allocate estimated best suitable harvesting methods to plots, while concurrently considering hauling route limitations, extraction route properties and stand characteristics. Our approach was based on productivity models and supported with expert-defined decision trees. The evaluation of the estimated best suitable harvesting methods and the comparison with the currently applied harvesting methods were completed for all 6500 National Forest Inventory (NFI) plots in Switzerland. We draw the following three major conclusions from our study: First, our modeling approach is an effective method to allocate estimated best suitable harvesting methods to NFI plots. Second, applying estimated best suitable harvesting methods would lead to cost reductions, in particular in the regions that include steep terrain and where harvesting mainly relies on cable- and air based extraction methods. Third, assuming an average timber price of 75 CHF m −3, 64 % instead of 52 % of the forest area could be harvested economically over the whole country if estimated best suitable methods were applied. This advantage would mainly be caused by a shift towards more mechanized harvesting methods. Improving the cost-effectiveness of the forestry sector is of high global relevance, as the increased use of domestic timber resources is a cost-efficient way to reduce atmospheric carbon emissions. The methodological framework described here was developed for Switzerland in particular, but it could be applied to Central Europe and other parts of Europe with a large amount of mountain forests.