Search Results (113)

Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 10⁸ m³, which closely aligns with contemporaneous statistical records. The model yielded an R² of 0.727, an RMSE of 537.566 m³, and a MAE of 239.767 m³, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R² from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article

Forests grow under dynamic conditions influenced by vegetation structure and environmental factors. However, empirical models to enhance growing stock volume GSV) estimation are commonly established based on structural information from airborne laser scanning (ALS) data, raising important questions regarding the models’ performance across time (temporal transferability). This study presents the integration of ALS and microclimate and site-specific data to assess the temporal transferability of GSV models at the plot level in a mixed forest located in Milicz, Poland, between 2007 (t₁) and 2015 (t₂). We compared random forest (RF), multiple linear regression (MLR), and generalized additive models (GAMs) across three modelling scenarios, ALS + site type + climate (sa), ALS only (sb), and ALS + site type (sc), and also performed internal and external validation to assess temporal transferability. Among the three modelling approaches, GAMs outperformed the MLR and RF models in internal validation, improving the R² by 6%–8% and reducing the rRMSE by 6%–12%. We found that climate was significant in GSV prediction when integrated with ALS and site conditions, with a permutation test (p ≤ 0.023) based on the rRMSE confirming climate significance. The direct contribution of climate to model performance was marginal on a broad scale. However, its influence on GSV and temporal transferability seem stronger in homogenous sites. In general, RF was the most stable in both the forward (t₁→t₂) and backward (t₂→t₁) directions in the sa scenario unlike the GAM, which was more stable in the backward direction. This study provides a framework for assessing the reliability of GSV models and addresses a critical gap in forest monitoring. Full article

Currently, it is very important to accurately estimate growing stock volumes; it is crucial for quantitatively assessing forest growth and formulating forest management plans. It is convenient and quick to use the Structure from Motion (SfM) algorithm in computer vision to obtain 3D point cloud data from captured highly overlapped stereo photogrammetry images, while the optimal algorithm for estimating growing stock volume varies across different data sources and forest types. In this study, the performance of UAV stereo photogrammetry (USP) in estimating the growing stock volume (GSV) using three machine learning algorithms for a coniferous plantation in Northern China was explored, as well as the impact of point density on GSV estimation. The three machine learning algorithms used were random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM). The results showed that USP could accurately estimate the GSV with R² = 0.76–0.81, RMSE = 30.11–35.46, and rRMSE = 14.34%–16.78%. Among the three machine learning algorithms, the SVM showed the best results, followed by RF. In addition, the influence of point density on the estimation accuracy for the USP dataset was minimal in terms of R², RMSE, and rRMSE. Meanwhile, the estimation accuracies of the SVM became stable with a point density of 0.8 pts/m² for the USP data. This study evidences that the low-density point cloud data derived from USP may be a good alternative for UAV Laser Scanning (ULS) to estimate the growing stock volume of coniferous plantations in Northern China. 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

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m² were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty. Full article

This study evaluates the performance of the ZEB Horizon RT portable mobile laser scanner (MLS) in simulating silvicultural thinning operations across three different Tuscan forests dominated by Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), Italian cypress (Cupressus sempervirens L.), and Stone pine (Pinus pinea L.). The aim is to compare the efficiency and accuracy of the MLS with traditional dendrometric methods. The study established three marteloscopes, each covering a 50 m × 50 m plot area (0.25 ha). Traditional dendrometric methods involved a team georeferencing trees using a total station and measuring the diameter at breast height (DBH) and selected tree heights (H) to calculate the growing stock volume (GSV). The MLS survey was carried out by a two-person team, who processed the point cloud data with LiDAR 360 software to automatically identify the tree positions, DBH, and H. The methods were compared based on the time, cost, and simulated felling volume. The MLS method was more time-efficient, saving nearly one and a half hours per marteloscope, equivalent to EUR 170. This advantage was most significant in denser stands, especially the Italian cypress forest. Both methods were comparable in terms of accuracy for Douglas-fir and Stone pine stands, with no significant differences in felling number or volume, although greater differences were noted for the Italian cypress forest. Full article

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. Full article

The aim of the study is to quantify the impacts of a possible transition to close-to-nature forestry in Slovakia and to compare the expected development of the total volume production, growing stock, merchantable wood increment and harvesting possibilities of forests in Slovakia with current conventional management using the FCarbon forest-growth model and available data from the Information System of Forest Management. The subject of the study was all forest stands available for wood supply (FAWS). The simulations were run in annual iterations using tree input data aggregated over 10-year-wide age classes. The calculation of wood increments was based on available growth models. In the business-as-usual (BAU) scenario, stock losses were based on the actual intensity of wood harvesting in the reference period 2013–2022. In the scenario of the transition to close-to-nature forest management, the losses were specifically modified from the usual harvesting regime at the beginning, to the target harvesting mode in selective forest at the end of the simulated period. With the modelling method used, a gradual increase in forest stocks occurred in both evaluated scenarios in the monitored period, namely by 10% in the case of BAU and by 23% in the case of close-to-nature forest management until 2050. In absolute mining volume, CTNF is by 5–10% lower than BAU management, with the difference gradually decreasing. The results show that the introduction of close-to-nature forest management will temporarily reduce the supply of wood to the market, but this reduction will not be significant and will be compensated by a higher total volume production, and thus also by increased carbon storage in forests. Full article

A peat substrate is made from peat from drained peatlands, which is a limited resource. A realistic estimate is that 50% of the world’s wetlands have been lost. Peat is used in horticulture, especially for the cultivation of vegetables in greenhouses. The consequences of peatland exploitation are an increase in the greenhouse effect and a decrease in carbon stocks. Wood fiber can be used as an alternative to peat. The chemical properties of growing media interact and change continuously due to the small volume of growing media, which is limited by the growing container. This study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with a peat substrate on the content and uptake of nutrients required by plants. The cucumber (Cucumis sativus L.) variety ‘Dirigent H’ developed in the Netherlands was cultivated in growing media of a peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) conventional fertilization (CF); (2) 13 g N per plant (N₁₃); (3) 23 g N per plant (N₂₃); (4) 30 g N per plant (N₃₀). The experiment was carried out with three replications. As the amount of wood fiber increased, the humidity and pH of the growing media increased. The fertilization of the cucumbers with different quantities of nitrogen influenced the nutrient uptake. The plants grown in the 50/50 and 25/75 growing media had the best Cu uptake when fertilized with N₂₃. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N₁₃, N_23, and N₃₀, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by the plants grown not only in the wood fiber, but also in the combinations with a peat substrate. Growing plants in wood fiber and fertilizing them with N₁₃ can result in the optimum uptake of micronutrients. The number and biomass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and biomass of fruits per plant obtained were significantly higher when the cucumbers were grown in WF/PS 50/50 growing media with additional N₁₃ fertilization. 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

Tropical forests play an important role in mitigating global climate change, emphasizing the need for reliable estimates of forest carbon stocks at regional and global scales. This is essential for effective carbon management, which involves strategies like emission reduction and enhanced carbon sequestration through forest restoration and conservation. However, reliable sample-based estimations of forest carbon stocks require accurate allometric equations, which are lacking for the rainforests of the Atlantic Forest Domain (AFD). In this study, we fitted biomass equations for the three main AFD forest types and accurately estimated the amount of carbon stored in their above-ground biomass (AGB) in Rio de Janeiro state, Brazil. Using non-destructive methods, we measured the total wood volume and wood density of 172 trees from the most abundant species in the main remnants of rainforest, semideciduous forest, and restinga forest in the state. The biomass and carbon stocks were estimated with tree-level data from 185 plots obtained in the National Forest Inventory conducted in Rio de Janeiro. Our locally developed allometric equations estimated the state’s biomass stocks at 70.8 ± 5.4 Mg ha⁻¹ and carbon stocks at 35.4 ± 2.7 Mg ha⁻¹. Notably, our estimates were more accurate than those obtained using a widely applied pantropical allometric equation from the literature, which tended to overestimate biomass and carbon stocks. These findings can be used for establishing a baseline for monitoring carbon stocks in the Atlantic Forest, especially in the context of the growing voluntary carbon market, which demands more consistent and accurate carbon stock estimations. Full article

Low-altitude aerial photogrammetry can be an alternative source of forest inventory data and a practical tool for rapid forest attribute updates. The availability of low-cost unmanned aerial systems (UASs) and continuous technological advances in terms of their flight duration and automation capabilities makes these solutions interesting tools for supporting various forest management needs. However, any practical application requires a priori empirical validation and optimization steps, especially if it is to be used under different forest conditions. This study investigates the influence of the main flight parameters, i.e., ground sampling distance and photo overlap, on the performance of individual tree detection (ITD) stand-level forest inventories, based on photogrammetric data obtained from budget unmanned aerial systems. The investigated sites represented the most common forest conditions in the Polish lowlands. The results showed no direct influence of the investigated factors on growing stock volume predictions within the analyzed range, i.e., overlap from 80 × 80 to 90 × 90% and GSD from 2 to 6 cm. However, we found that the tree detection ratio had an influence on estimation errors, which ranged from 0.6 to 15.3%. The estimates were generally coherent across repeated flights and were not susceptible to the weather conditions encountered. The study demonstrates the suitability of the ITD method for small-area forest inventories using photogrammetric UAV data, as well as its potential optimization for larger-scale surveys. Full article

The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km² in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R² and an RMSE% of 0.67 and 41%, respectively. Instead, an R² of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models’ performances, yielding significantly higher R² values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model. Full article

Winery wastewater (WW) is a high-volume biowaste and, in the context of Marlborough and New Zealand wineries, there is a growing recognition of the need to improve current WW disposal systems to mitigate negative environmental impacts. The application of WW to land is a low-cost method of disposal, that could significantly reduce the environmental risk associated with WW directly entering surface and groundwater bodies. This study analysed elemental concentrations in WW and soils from three Marlborough vineyards across their annual vintage to determine the loading rates of nutrients into WW and the subsequent accumulation effects of WW irrigation on receiving soils. The findings showed loading rates of approximately 1.8 t ha⁻¹ yr⁻¹ of sodium within WW and a significant increase in soil sodium concentration and pH, attributed to sodium-based cleaning products. A loading rate of approximately 4 t ha⁻¹ yr⁻¹ of total organic carbon was also identified within WW, however, significant losses in soil carbon, nitrogen, magnesium and calcium concentrations were identified. Focusing efforts to retain key nutrients from WW within soils could provide benefits to New Zealand’s wine industry, facilitating increased biomass production in irrigation plots, thereby increasing biodiversity and potentially generating incentives for vineyard owners to contribute to increasing biomass carbon stocks and offset agricultural greenhouse gas emissions. Full article

Forest growing stock volume is a crucial indicator for assessing forest resources. However, contemporary machine learning models used in estimating forest growing stock volume often exhibit fluctuating precision and are confined to specific tree species, lacking universality. This limitation impedes their capacity to provide comprehensive forest survey services. This study designed a novel model for predicting forest growing stock volume named RF-Adaboost. The model represented the inaugural application of the Adaboost algorithm in estimating forest growing stock volume. Additionally, the authors innovatively refined the Adaboost algorithm by integrating Random Forest as its weak learner. To substantiate the model’s effectiveness, the authors designed three data combination schemes at different scales and conducted regression estimation using the RF-Adaboost model, traditional Random Forest, and Adaboost models, respectively. The results indicated that the RF-Adaboost model consistently outperforms others across various data schemes. Furthermore, utilizing a combined data scheme of remote sensing and Continuous Forest Inventory, the RF-Adaboost model demonstrated optimal performance in estimating forest growing stock volume (R² = 0.81, RMSE = 7.08 m³/site, MAE = 3.36 m³, MAPE = 8%). Finally, the RF-Adaboost model exhibits greater universality, eliminating the need for strict differentiation between tree species. This research presented an efficient and cost-effective approach to estimate forest growing stock, addressing the challenges associated with conventional survey methods. Full article