Search Results (76)

National Forest Inventories (NFIs) offer a comprehensive and consistent dataset for forest analysis, enabling the refinement of growth and yield models by integrating regional environmental factors. This study investigates the influence of climate on the growth of three dominant tree species in Romania: Norway spruce (Picea abies L. Karst), European beech (Fagus sylvatica L.), and Sessile oak (Quercus petraea (Matt.) Liebl). Increment core analysis revealed a general increase in diameter growth since 1960, partially correlated with temperature trends. Repeated measures analysis confirmed significant variations in radial growth across ecoregions. The analysis further explored the impact of climatic variables on diameter at breast height (DBH) and basal area (BA) growth and yield. Among nine climatic attributes and their combinations, total precipitation and average growing season temperature significantly affected DBH and BA growth. However, yield was largely insensitive to precipitation, with only Sessile oak yield showing a temperature dependence. Beyond ecoregion and climate, the growth and yield of DBH and BA exhibited positive correlations with the calendar year, age, and previous growth/yield values. Notably, DBH and BA growth demonstrated a dependence on the preceding four to five years, whereas yield was significantly influenced only by the previous year. The observed influence of both the calendar year and previous years suggests a prolonged environmental memory in tree growth and yield responses. Full article

Forest development forms the foundation for the advancement of sustainable forest management that integrates the knowledge of natural and anthropogenic processes with ecological and biological insights. This study aims to emphasize the role of assisted natural regeneration and balanced forest development phases in fostering closer-to-nature management approaches, contributing to resilient forest ecosystems capable of self-regulation and biodiversity support in the face of anthropogenic and climatic challenges. This study focuses on forest development in Lithuania based on five National Forest Inventories (NFIs) from 2002 to 2022. We examine the tree volume structure of the growing stock by stand type and forest type series from the point of view of stand age and forest development phases. This is performed by applying the standardized methodologies of the Lithuanian National Forest Inventory. Our analysis focuses on broader patterns derived from the selected NFI data rather than stand-level details. Our findings demonstrate that long-term observation of dynamic National Forest Inventories can aid in the development of closer-to-nature forest management methods for different forest type series. In order to implement the European Union’s strategy and policy for closer-to-nature forest management, we call for the use of “assisted succession” methods in commercial forests, promoting the formation of mixed-species forest stands with multi-cohort age profiles, including old-growth all-aged forest patches of >121 years. Full article

Forest ecosystems are important for biodiversity conservation, climate regulation and climate change mitigation, soil and water protection, and the recreation and provision of raw materials. This paper presents a dataset on forest type and tree species composition for 934 georeferenced plots located in Italy. The forest type is classified in the field consistently with the Italian National Forest Inventory (NFI) based on the dominant tree species or species group. Tree species composition is provided by the percent crown cover of the main five species in the plot. Additional data on conifer and broadleaves pure/mixed condition, total tree and shrub cover, forest structure, sylvicultural system, development stage, and local land position are provided. The surveyed plots are distributed in the central–eastern Alps, in the central Apennines, and in the southern Apennines; they represent a wide range of species composition, ecological conditions, and silvicultural practices. Data were collected as part of a project aimed at developing a classification algorithm based on hyperspectral data. The dataset was made publicly available as it refers to forest types and species widespread in many countries of Central and Southern Europe and is potentially useful to other researchers for the study of forest biodiversity or for remote sensing applications. Full article

National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approaches help address this issue by reducing dependence on fully annotated images and leveraging unlabeled data. However, their potential for large-scale LC mapping needs further investigation. This study explored the use of NFI data with deep learning and weakly supervised and self-supervised methods. Using Sentinel-2 images and the Portuguese NFI, which covers other LC types beyond forest, as sparse labels, we performed weakly supervised semantic segmentation with a convolutional neural network to create an updated and spatially continuous national LC map. Additionally, we investigated the potential of self-supervised learning by pretraining a masked autoencoder on 65,000 Sentinel-2 image chips and then fine-tuning the model with NFI-derived sparse labels. The weakly supervised baseline achieved a validation accuracy of 69.60%, surpassing Random Forest (67.90%). The self-supervised model achieved 71.29%, performing on par with the baseline using half the training data. The results demonstrated that integrating both learning approaches enabled successful countrywide LC mapping with limited training data. Full article

Natural mixed forests’ carbon sequestration capacity is crucial for mitigating climate change and maintaining ecological balance. However, most of the current studies only consider the role of forest age, ignoring the influence of carbon growth grade and stand structural diversity, which leads to an increase in uncertainty in large-scale forest carbon sink assessment. The aim of this study was to quantify the effects of carbon growth grade and stand structure diversity on the carbon sink of natural mixed forests and to establish a more accurate stand carbon growth model. Based on sample data from the National Forest Inventory (NFI) of China, the stand carbon growth model was established based on Gompertz and Logistic theoretical growth models, and the forest carbon sink at the regional scale was predicted. It was found that the stand carbon growth model considering only the stand age as a single variable often had poor results, with R² less than 0.36, while R² values of the optimal model introducing carbon growth grade and stand structural diversity were 0.87 and 0.48, respectively, which significantly improved the prediction accuracy of the model, and both had significant effects on stand carbon stocks. By predicting the future forest carbon sink, it was found that the forest carbon sink of the natural coniferous–broadleaved mixed forests in Jilin Province would reach 791 (781–801) t c/a and 843 (833–852) t c/a in 2030 and 2060, respectively, which were 17% lower and 51% higher than that of the forest carbon sink estimated by considering only the age. Moreover, the model considering structural diversity predicted a more positive carbon sink trend, indicating that forest carbon stocks could be more effectively maintained and carbon sinks increased by increasing the complexity of stand diameter at breast height structure, which has important guiding significance for future forest carbon sink management. This study provides scientific support for achieving the goal of “carbon neutrality” proposed by China. Full article

The sources of uncertainty in wall-to-wall AGB maps propagate from the tree to pixel, but uncertainty due to forest cover mapping is rarely incorporated into the error propagation process. This study aimed to (1) elaborate an analytical procedure to incorporate forest-mapping-related uncertainty into the error propagation from plot and pixel predictions; (2) develop a stratified estimator with a model-assisted estimator for small and large areas; and (3) estimate the effect of ignoring the mapping uncertainty on the confidence intervals (CIs) for totals. Data consist of a subset of the Brazilian national forest inventory (NFI) database, comprising 75 counties that, once aggregated, served as strata for the stratified estimator. On-ground data were gathered from 152 clusters (plots) and remotely sensed data from Landsat-8 scenes. Four major contributions are highlighted. First, we describe how to incorporate forest-mapping-related uncertainty into the CIs of any forest attribute and spatial resolution. Second, stratified estimators perform better than non-stratified estimators for forest area estimation when the response variable is forest/non-forest. Comparing our stratified estimators, this study indicated greater precision for the stratified estimator than for the regression estimator. Third, using the ratio estimator, we found evidence that the simple field plot information provided by the NFI clusters is sufficient to estimate the proportion forest for large regions as accurately as remote-sensing-based methods, albeit with less precision. Fourth, ignoring forest-mapping-related uncertainty erroneously narrows the CI width as the estimate of proportion forest area decreases. At the small-area level, forest-mapping-related uncertainty led to CIs for total AGB as much as 63% wider in extreme cases. At the large-area level, the CI was 5–7% wider. Full article

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

The accurate large-scale classification of tree species is crucial for the monitoring, protection, and management of the Earth’s invaluable forest ecosystems. Numerous previous studies have recognized the suitability of satellite imagery, particularly Sentinel-2 imagery, for this task. In this study, we utilized a dense phenology Sentinel-2 time series, which offered consistent data across multiple granules, to map tree species across the entire forested area in Austria. Aiming for the classification scheme to more accurately represent actual forest conditions, we included mixed tree species and sparsely populated classes (classes with sparse canopy cover) alongside pure tree species classes. To enhance the training data for the mixed and sparse classes, synthetic data creation was employed. Autocorrelation has significant implications for the validation of thematic maps. To investigate the impact of spatial dependency on validation data, two methods were employed at numerous split and buffer distances: spatial split validation and a validation method based on a buffered ground reference probability samples provided by the National Forest inventory (NFI). While a random training data holdout set yielded 99% accuracy, the spatial split validation resulted in 74% accuracy, emphasizing the importance of accounting for spatial autocorrelation when validating with holdout sets derived from polygon-based training data. The validation based on NFI data resulted in 55% overall accuracy, 91% post-hoc pure class accuracy, and 79% accuracy when confusions in phenological proximity were disregarded (e.g., spruce–larch confused with spruce). The significant differences in accuracy observed between spatial split and NFI validation underscore the challenge for polygon-based training data to capture ground reference forest complexity, particularly in areas with diverse forests. This hardship is further accentuated by the pure class accuracy of 91%, revealing the substantial impact of mixed stands on the accuracy of tree species maps. Full article

Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area’s strategic information, the accuracy is considered adequate. Full article

The stand basal area, closely related to age, site quality, and stand density, is an important factor for predicting forest growth and yield. The accurate estimation of site quality is especially a key component in the stand basal area model. We utilized sample plots with Picea asperata Mast. as the dominant species in the multi-period National Forest Inventory (NFI) dataset to establish a site index (SI) model including climate effects through the difference form of theoretical growth equations and mixed-effects models. We combined the SI calculated from the SI model, stand age, and stand density index to construct a basal area growth model for Picea asperata Mast. stands. The results show that the Korf model is the best SI base model for Picea asperata Mast. The mean temperatures in summer and winter precipitation were used as the fixed parameters to construct a nonlinear model. Ultimately, elevation, origin, and region, as random effects, were incorporated into the mixed-effects model. The coefficients (R²) of determination of the base model, the nonlinear model including climate, and the nonlinear mixed-effects model are 0.869, 0.899, and 0.921, with root-mean-square errors (RMSEs) of 1.320, 1.315, and 1.301, respectively. Among the basal area models, the Richards model has higher precision. And the basal area model including an SI incorporating climatic factors had a higher determination coefficient (R²) of 0.918 than that of the model including an SI without considering climatic effects. The mixed-effects model incorporating climatic and topographic factors shows a better fitting performance of SI, resulting in a higher precision of the basal area model. This indicates that in the development of forest growth models, both biophysical and climatic factors should be comprehensively considered. Full article

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm⁻² to 25.313 t·hm⁻². We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models. Full article

Norway spruce stands are established and managed along various site conditions in central Europe. Currently, spruce often grows at locations outside of its ecological optimum, resulting in extensive damage elicited by harmful abiotic and biotic factors, which relatively shortens the time to change this adverse status in the adaptation frame by foresters. Except for the rapid change in species composition through clear-cuts, another way is possible, i.e., stabilising current (especially young) spruce stands to extend the time required to implement adaptation measures. The assumption that different site conditions will have to be respected as part of this adaptation was confirmed by our study based on NFI data of the Czech Republic. A semiparametric generalized linear model (GAM) was used to model the relationship between the height-to-diameter ratio and forest stand age, differentially considering particular forest vegetation zones. Spruce stands with lower elevations attain a lower stability (expressed by their height-to-diameter ratio; HDR) than those in the mountains. The HDR culminated in lower and middle altitudes in the first half of the rotation period, representing the most critical timing and effectivity of silvicultural measures. Contrary to previous findings, we found higher HDR values at nutrient-rich sites than those at acid ones, especially up to 50–60 years old. Therefore, more research should be devoted to the issue concerning the same thinning regime under different site conditions. Full article

Our objective was to develop a method for the assessment of forest area and structural variables for cases in which the availability of representative ground reference data is poor and these data are not collected from the whole area of interest. We implemented two independent approaches to the estimation of the forest variables of a European boreal forest: (i) the computation of wall-to-wall estimates using moderate- to low-resolution VIIRS imagery from the Suomi NPP mission; and (ii) the visual interpretation of plots of samples from very high resolution (VHR) satellite data obtained via a two-stage design. Our focus was on the statistical comparison of forest resources at a country or larger level. The study area was boreal forest ranging from Norway to the Ural Mountains in Russia. We computed a seamless mosaic from 111 VIIRS images. From the mosaic, we computed predictions for the forest area, growing stock volume, height of the dominating tree layer, proportion of conifers and broadleaved trees, site fertility class, and leaf area index. The reference data for the VIIRS imagery were national forest inventory (NFI)-based raster maps from Finland. The first stage sample of VHR data included 42 images; of these, a second stage sample of 2690 plots was visually interpreted for the same variables. The forest area prediction from VIIRS for the whole study area was 1.2% higher than the VHR-based result. All other structural variable predictions using VIIRS fitted within the 95% confidence intervals computed from the VHR sample except for estimates of the main tree species groups, which were outside the limits. A comparison of VIIRS-based forest area estimates using Finnish and Swedish NFI data indicated overestimations of 10.0% points and 4.6% points, whereas the total growing stock volumes were overestimated by 8% and underestimated by 3.4%, respectively. The correlation coefficients between the VIIRS and VHR image predictions at the 42 VHR image locations varied from 0.70 to 0.85. The VIIRS maps strongly averaged the local predictions due to their coarse spatial resolutions. Based on our findings, the approach using two independent estimations yielded similar figures for the central forest variables for the European boreal forest. A model computed using reference data from a small part of the area of interest can provide satisfactory predictions for a much larger area with a similar biome. Therefore, our concept is applicable to the estimation and overall mapping of a forest area and central structural variables at regional to national levels. Full article

Forest age is a critical parameter for the status and potential of carbon sequestration in forest ecosystems and reflects major forest disturbance information. However, reliable forest age data with high spatial resolution are lacking to date. In this study, we proposed a forest age mapping method with a 30 m resolution that considers forest disturbance. Here, we used the Landsat time-series stacks (LTSS) data from 1986 to 2021 and implemented the LandTrendr algorithm on the Google Earth Engine (GEE) platform to detect the age of disturbed forests. The age of non-disturbed forests was extracted based on forest canopy height data and the empirical relationship between age and height. High-resolution Google images combined with the forest management archive data of forestry departments and national forest inventory (NFI) data were used for the validation of disturbed and non-disturbed forest age, respectively. The results showed that the LandTrendr algorithm detected disturbance years with producer and user accuracies of approximately 94% and 95%, respectively; and the age of non-disturbed forests obtained using the empirical age–height relationship showed an R² of 0.8875 and a root mean squared error (RMSE) value of 5.776 with NFI-based results. This confirms the reliability of the proposed 30 m resolution forest age mapping method considering forest disturbance. Overall, the method can be used to produce spatially explicit forest age data with high resolution, which can contribute to the sustainable use of forest resources and enhance the understanding of carbon budget studies in forest ecosystems. Full article

In current forestry investigation studies, the research hotspots have tended to concentrate on ascertaining the precision of certain tree parameters. This has resulted in an augmented intricacy of the technique in terms of algorithms and observation instruments. The complexity of the technology and the cost of the equipment make it impossible to use for large-scale forest surveys, for example, a national forest inventory (NFI). The aim of our study was to design a new type of low-cost measuring method that could be utilized in a NFI and in developing countries. Meanwhile, the newly designed method was expected to be able to output certain forest measurement factors without necessitating data processing by NFI field investigators. Based on these objectives, we developed a measuring method that included hardware comprised of two tools. The first tool was an electronic measuring tape that contained a microcontroller unit (MCU) and could automatically record and collaborate with other equipment via wireless protocols. The second tool was a tree stem position mapper that utilized our own designed mechanisms. The results showed that the tree DBH measurements exhibited a 0.05 cm (0.20%) bias and a 0.36 cm (1.45%) root mean square error (RMSE), and the biases on the x-axis and the y-axis of the tree position estimations were −15.92–9.92 cm and −25.90–10.88 cm, respectively, accompanied by corresponding RMSEs of 15.27–29.40 cm and 14.49–34.68 cm. Moreover, an efficiency test determined that the average measurement time per tree was 20.34 s, thus, demonstrating a marked improvement in speed by nearly one-fold compared to the conventional method. Meanwhile, this measurement kit costs less than 150 Euros and is economically suitable for large-scale applications. We posit that our method has the potential to serve as a standard tool in a Chinese NFI and in developing countries in the future. Full article