Search Results (25)

This study investigates the effects of different land-use types (forest, arable land, and wetland) on key soil properties, microbial communities, and nitrogen cycling in the Lesser Khingan Mountains. The results revealed that forest (FL) and wetland (WL) soils had significantly higher soil organic matter (SOM) content compared with arable land (AL), with total phosphorus (TP) being highest in FL and available nitrogen (AN) significantly higher in WL. In terms of enzyme activity, AL and WL showed reduced activities of ammonia monooxygenase (AMO), β-D-glucosidase (β-G), and β-cellobiosidase (CBH), while exhibiting increased N-acetyl-β-D-glucosaminidase (NAG) activity, highlighting the impact of land use on nitrogen dynamics. WL also exhibited significantly higher microbial diversity and evenness compared with FL and AL. The dominant bacterial phyla included Actinobacteriota, Proteobacteria, and Acidobacteriota, with Acidobacteriota being most abundant in FL and Proteobacteria most abundant in WL. Network analysis showed that AL had the most complex and connected microbial network, while FL and WL had simpler but more stable networks, suggesting the influence of land use on microbial community interactions. Regarding nitrogen cycling genes, AOA-amoA was most abundant in AL, while AOB-amoA was significantly enriched in FL, reflecting the influence of land use on ammonia oxidation. These findings highlight how land-use types significantly affect soil properties, microbial community structures, and nitrogen cycling, offering valuable insights for sustainable land management. Full article

The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling of trees by adding a quantitative analysis capability to acquire stand factors. We used unmanned aircraft LiDAR (ALS) data as the raw data for this study. After denoising the data and segmenting the single trees, we obtained the single-tree samples needed for this study and produced our own single-tree sample dataset. The scanned tree point cloud was reconstructed in three dimensions in terms of geometry and topology, and important stand parameters in forestry were extracted. This improvement in the quantification of model parameters significantly improves the utility of the original point cloud tree reconstruction algorithm and increases its ability for quantitative analysis. The tree parameters obtained by this improved model were validated on 82 camphor pine trees sampled from the Northeast Forestry University forest. In a controlled experiment with the same field-measured parameters, the root mean square errors (RMSEs) and coefficients of determination (R²s) for diameters at breast height (DBHs) and crown widths (CWs) were 4.1 cm and 0.63, and 0.61 m and 0.74, and the RMSEs and coefficients of determination (R²s) for heights at tree height (THs) and crown base heights (CBHs) were 0.55 m and 0.85, and 1.02 m and 0.88, respectively. The overall effect of the canopy volume extracted based on the alpha shape is closest to the original point cloud and best estimated when alpha = 0.3. Full article

The impact of nitrogen and phosphorus deposition alternations, as well as apoplastic litter quality and quantity, on soil nutrient cycling and soil carbon pool processes in forest ecosystems is of considerable importance. Soil ecological enzyme chemistry is a powerful tool for elucidating the nutrient limitations of microbial growth and metabolic processes. In order to explore the responding mechanisms of soil ecological enzyme chemistry to the simultaneous changes in apoplast input and nitrogen and phosphorus deposition in temperate coniferous and broad-leaved mixed forests, an outdoor simulating experiment was conducted. The results demonstrate that the treatments involving apoplastic material and nitrogen and phosphorus additions had significantly impacted soil nutrient levels across different forest types. Apoplastic treatments and N-P additions had a significant effect on the soil total organic carbon (TOC), dissolved organic carbon (DOC), soil total soluble nitrogen (TSN), soil available phosphorus (SAP), soil total nitrogen (TN), soil total phosphorus (TP), and microbial biomass carbon (MBC). However, the effects on soil microbial biomass (MBN) and microbial biomass phosphorus (MBP) were insignificant. The apomictic treatments with N and P addition did not result in a statistically significant change in soil C-hydrolase activities (β-1,4-glucosidase BG, β-1,4-xylosidase BX, cellobiohydrolase CBH, phenol oxidase POX, and peroxidase PER), N-hydrolase activities (β-1,4-N-acetylglucosaminidase NAG and L-leucine aminopeptidase LAP), or P-hydrolase activities (Acid phosphatase AP). Although the apomictic treatments did not yield a significant overall impact on carbon hydrolase activity, they influenced the activity of specific enzymes, such as CBH, LAP, and PER, to varying degrees. The effects on BG, BX, CBH, AP, and C-hydrolase activities were significant for different stand types. The impact of apomictic treatments and N-P additions on soil nitrogen hydrolase activities was inconsequential with a minimal interactive effect. The highest correlation between PER, LAP, and N-hydrolase activities was observed in conjunction with elevated levels of nitrogen and phosphorus addition (N3L0, original litter treatment, and high amounts of N and P addition). These findings may provide a theoretical foundation for the management of ecosystem function in broad-leaved Korean pine forests. Full article

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring. Full article

Increasing nitrogen (N) deposition alters the availability of soil nutrients and is likely to intensify phosphorus (P) limitations, especially in P-limited tropical and subtropical forests. Soil microorganisms play vital roles in carbon (C) and nutrient cycling, but it is unclear whether and how much N and P imbalances affect the soil’s microbial metabolism and mechanisms of nutrient limitations. In this study, a 3-year field experiment of N and P addition (control (CK), 100 kg N ha⁻¹ yr⁻¹ (N), 50 kg P ha⁻¹ yr⁻¹ (P), and NP) was set up to analyze the extracellular enzyme activities and stoichiometry characteristics of the top mineral soils in Chinese fir plantations with different stand ages (7, 20, and 33 years old). The results showed that the enzyme activities associated with the acquisition of C (β-1,4-glucosidase (BG) and β-d-cellobiohydrolase (CBH)) and P (acid phosphatases (APs)) in the N treatment were significantly higher than those in the CK treatment. Moreover, vector analysis revealed that both the vector’s length and angle increased in stands of all ages, which indicated that N addition aggravated microbial C and P limitations. The P and NP treatments both significantly decreased the activity of AP and the enzymes’ N:P ratio, thereby alleviating microbial P limitations, as revealed by the reduction in the vector’s angle. Stand age was found to promote all enzymatic activities but had no obvious effects on the limitation of microbial metabolism with or without added nutrients in the soils under Chinese fir. Available N, Olsen-P, and pH were the main drivers of microbial metabolic limitations related to C nutrients. These results provide useful data for understanding the change in soil microbial activity in response to environmental changes, and suggest that P fertilization should be considered for management to improve productivity and C sequestration in Chinese fir plantation in the context of increased deposition of N. Full article

Elucidating the characteristics and underlying mechanisms of soil organic carbon (SOC) and nitrogen mineralization in the context of sludge addition is vital for enhancing soil quality and augmenting the carbon sink capacity of soil. This study examined the chemical properties, enzyme dynamics, and organic carbon and nitrogen mineralization processes of soil from degraded grasslands on the Loess Plateau at various incubation temperatures (5, 15, 25, and 35 °C) and sludge addition rates (0%, 5.0%, 10.0%, and 20.0%) through a laboratory incubation experiment. The results showed that incubation temperature, sludge addition, and their interactive effects significantly altered the soil enzyme C:N, C:P, and N:P stoichiometries. The cumulative mineralization rates of SOC and nitrogen increased significantly with increasing incubation temperature and sludge addition rate. Principal component analysis revealed a significant linear correlation between cumulative SOC and nitrogen mineralization. Random forest analysis indicated that β-1,4-Glucosidase (BG), β-1,4-N-acetyglucosaminidase (NAG), cellobiohydrolase (CBH), ammonium nitrogen (NO₃⁻), enzyme C:P ratio, alkaline phosphatase (ALP), and incubation temperature were crucial determinants of cumulative SOC mineralization. Structural equation modeling demonstrated that sludge addition, NO₃⁻, NAG, ALP, and enzyme C:P positively impacted SOC mineralization, whereas dissolved organic carbon and BG had negative impacts. Conversely, incubation temperature negatively affected soil nitrogen mineralization, whereas NO₃⁻, available phosphorus, and ALP contributed positively. Sludge addition and temperature indirectly modulated soil net nitrogen mineralization by altering soil chemical properties and enzyme activities. These findings underscore the role of SOC and nitrogen mineralization as indicators for evaluating soil nutrient retention capabilities. Full article

Precise estimates of dendrometric and morphometric variables are indispensable for effective forest resource conservation and sustainable utilization. This study focuses on modeling the relationships between shape (morphometric), dimension (dendrometric) and density (N) to assess the sustainability of forest resources. It sheds light on the current state of site characteristics, reproduction, and the structure of Araucaria angustifolia trees at selected forest remnants across multiple sites in Santa Catarina, Southern Brazil. Individual trees and their dendrometric variables, such as the diameter at breast height (d), height (h), crown base height (cbh), annual periodic increment (API) in growth rings, and morphometric variables, including four radii of the crown in cardinal directions, were evaluated. These measurements allowed us to calculate various morphometric indices and crown efficiency, enabling the assessment of both vertical and horizontal structural conditions. Statistical analysis confirmed a positive relationship of the crown volume (cv) and crown surface area (csa) with the crown length (cl). Conversely, the crown efficiency, density, increment rate, and reproductive structure production declined. These morphometric relationships emphasize the complex dynamics within these forest ecosystems, irrespective of the chosen site, indicating that horizontal and vertical forest structures have stagnated and have been characterized by limited change in the last ten years. Such results raise concerns about sustainability, highlighting the need for proper conservation measures and sustainable forest management practices. Our findings underscore the need for substantial adjustments in the structure and dynamics of the forest, particularly on selected rural properties where this tree species is abundant, to ensure long-term sustainability. Full article

In Brazil, logging in the Seasonally Dry Tropical Forest (SDTF) under management plans that include clear-cutting has increased in recent decades, and the structure, composition, diversity and functioning of the forest likely must have been affected. The aim of this study was to understand the growth dynamics of shrub–tree biomass (STB), species richness and vegetation structure as a function of regeneration time after clear-cutting (treatments), taking the Legal Reserve (40 years of regeneration) as reference. The study was carried out in 2018 at the Ramalhete Settlement, General Sampaio, in the state of Ceará. All plants with a circumference at breast height (CBH) ≥ 6 cm were identified and the CBH was measured across 42 sample plots (20.0 m × 20.0 m), using seven plots per treatment (3, 5, 8, 11 and 15 years after clear-cutting, and the Legal Reserve, 40 years of regeneration). The following were determined: STB (total and by species), density and basal area (by ecological group and diameter class), basal area (species of higher added value), diversity (Hill numbers), and the importance value index (IVI). It was found that during the early years (up to at least 11 years), many important forest characteristics related to the composition of the ecological groups and vegetation structure were strongly affected, and major impacts can be seen, the effects of which, however, decreased over time of regeneration, having almost no effect after 15 years. After 15 years following clear-cutting, the SDTF presented accumulated STB, species richness and structure similar to the area undergoing regeneration for 40 years. However, the small number of indicator species of more-preserved areas (even at T15 and T40) points out that management needs to be improved. However, promoting species of greater added value and determining whether the forest recovers its structure and diversity after successive cutting cycles also still need to be addressed. Full article

Soil enzyme activity can be affected by both production and degradation processes, as enzymes can be degraded by proteases. However, the impact of nutrient addition on enzyme activity is often solely attributed to changes in enzyme production without fully considering degradation. In this study, we demonstrate that the activities of β-1,4-glucosidase (BG), β-D-cellobiohydrolase (CBH), β-1,4-xylosidase (BX), and β-1,4-N-acetyl-glucosaminidase (NAG) in two tropical plantations exhibited comparable levels between nitrogen (N)- and phosphorus (P)-fertilized soils and the unfertilized control under field conditions. However, it was observed that the reduction in enzymatic activity was significantly higher in the fertilized soils during short-term laboratory incubation in the acacia plantation. Additionally, the eucalyptus plantation exhibited a similar tendency, although statistical significance was not achieved due to the high variance of the data. The results show that the interruption of the natural, continuous supply of organic matter or non-soil microbial-derived enzymes, which typically occurs under field conditions, leads to a more significant reduction in soil enzyme activities in fertilized soils compared to unfertilized control. This may be attributed to the higher abundance of protease in fertilized soils, resulting in faster enzyme degradation. Interestingly, P fertilization alone did not have a similar effect, indicating that N fertilization is likely the main cause of the larger decreases in enzyme activity during incubation in fertilized soils compared to unfertilized control soils, despite our study site being poor in P and rich in N. These findings highlight the importance of considering enzyme degradation when investigating material dynamics in forest ecosystems, including the impact of nutrient addition on enzyme activity, as enzyme production alone may not fully explain changes in soil enzyme activity. Full article

Surface downward longwave radiation (SDLR) is significant with regard to surface energy budgets and climate research. The uncertainty of cloud base height (CBH) retrieval by remote sensing induces the vast majority of SDLR estimation errors under cloudy conditions; reliable CBH observation and estimation are crucial for determining the cloud radiative effect. This study presents a CBH retrieval methodology built from 10 thermal spectral data from Himawari-8 (H-8) observations, utilizing the random forest (RF) algorithm to fully account for each band’s contribution to CBH. The algorithm utilizes only infrared band data, making it possible to obtain CBH 24 h a day. Considering some factors that can significantly affect the CBH estimation, RF models are trained for different clouds using inputs from multiple H-8 channels together with geolocation information to target CBH derived from CloudSat/CALIPSO combined measurements. The validation results reveal that the new methodology performs well, with a root-mean-square error (RMSE) of only 1.17 km for all clouds. To evaluate the effect of CBH on SDLR estimation, an all-sky SDLR estimation algorithm based on previous CBH predictions is proposed. The new SDLR product not only has a resolution that is noticeably higher than that of benchmark products of the SDLR, such as the Clouds and the Earth’s Radiant Energy System (CERES) and the next-generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), but it also has greater accuracy, with an RMSE of 21.8 W m⁻² for hourly surface downward longwave irradiance (SDLI). Full article

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m² each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R² between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R² using ALS data was achieved predicting CBH (R² = 0.98), while for TLS and ALS + TLS, the highest R² was observed predicting HT, CW, and CBD (R² = 0.94) and HT (R² = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management. Full article

There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm accuracy. Although the ML algorithm performs well under relatively simple single-layer volcanic ash cloud conditions, it tends to overestimate VBH in multi-layer conditions, which is an unresolved problem in meteorological CBH retrieval. Full article

Knowledge of the formation and correlation of reproductive structures with dendro/morphometric variables of the Araucaria angustifolia tree species is a tool for its conservation and viability for sustainable forest management. We counted visually in araucaria trees the number of male strobili in RGB images acquired by Remotely Piloted Aircraft System (RPAs) over forest remnants. The diameter at the breast height (d), total height (h), crown radii (cr), crown base height (cbh), periodic annual increment in d based on increment rolls were measured, and the morphometric indices and crown efficiency were calculated with these variables. The relationships of these variables with male strobili production were analyzed by Pearson’s correlation and multivariate analysis techniques (cluster, factorial analysis, and main components). The morphometric variables correlated with the production of male strobili were d (r = 0.58, p-0.0002), crown diameter (r = 0.62, p < 0.0001), crown area (r = 0.62, p < 0.0001), coverage index (r = 0.51, p-0.001) and slenderness (r = −0.39, p-0.01). We argue that the production of male strobili is related to the vitality, dimension, density, growth space, and position in the stratum of the tree inside the forest, inferring a relationship between reproductive structures with the shape, size, growth space, and tree density. Such aspects shall be considered in future forest management initiatives in Southern Brazil. Full article

Canopy fuel characterization is critical to assess fire hazard and potential severity in forest stands. Simulation tools provide useful information for fire prevention planning to reduce wildfire impacts, provided that reliable fuel maps exist at adequate spatial resolution. Free airborne LiDAR data are becoming available in many countries providing an opportunity to improve fuel monitoring at large scales. In this study, models were fitted to estimate canopy base height (CBH), fuel load (CFL) and bulk density (CBD) from airborne LiDAR in a pine stand area where four point-cloud datasets were acquired at different pulse densities. Best models for CBH, CFL and CBD fitted with LiDAR metrics from the 1 p/m² dataset resulted in an adjusted R² of 0.88, 0.68 and 0.58, respectively, with RMSE (MAPE) of 1.85 m (18%), 0.16 kg/m² (14%) and 0.03 kg/m³ (20%). Transferability assessment of fitted models indicated different level of accuracy depending on LiDAR pulse density (both higher and lower than the calibration dataset) and model formulation (linear, power and exponential). Best results were found for exponential models and similar pulse density (1.7 p/m²) compared to lower (0.5 p/m²) or higher return density (4 p/m²). Differences were also observed regarding the canopy fuel attributes. Full article

Drainage of freshwater wetlands is common in coastal regions, although the effects on microbial extracellular enzyme activity (a key mediator of soil organic matter decomposition) in relation to spatial variability (microtopography and soil depth) are poorly understood. Soils were collected from organic (Oi, Oe, Oa) and mineral (A, AB, B) horizons from a natural and drained coastal forested wetland in North Carolina, USA. Activity of seven enzymes were measured: α-glucosidase (AG), β-glucosidase (BG), cellobiohydrolase (CBH), xylosidase (XYL), phenol oxidase (POX), peroxidase (PER) and N-acetyl glucosamide (NAG). Enzyme activity rates were normalized by soil weight, soil organic C (SOC), and microbial biomass C (MBC). Specific enzyme activity (per SOC or MBC) was more sensitive to drainage and soil depth compared to normalization by soil weight. In Oi and Oa horizons, specific enzyme activity (per MBC) (AG, BG, XYL, POX, PER) was higher in the natural compared to drained wetland but lower (AG, CBH, XYL, POX, PER, NAG) in the AB or B mineral soils. Results from this study indicate that organic soil horizons of natural freshwater wetlands contain a highly active microbial community driven by inputs of plant-derived C, while deeper soils of the drained wetland exhibit higher microbial metabolic activity, which likely plays a role in SOC storage of these systems. Full article