Sample Plots Research Articles

STRUKTUR DAN KOMPOSISI VEGETASI HUTAN ALAM NEGERI AMAHUSU KECAMATAN NUSANIWE KOTA AMBON

This study aims to determine the structure and composition of the natural forest vegetation of Negeri Amahusu, Nusaniwe District, Ambon City. The sampling method used in this study is a combination of the grid line method and the path method to study the composition and structure of vegetation at the research location. In this study, 5 (five) observation paths were made, each path consisting of 5 plots with a size of 20 x 20 m for the tree level so that the observation path measures 20 x 100 m. While the sample plots for the pole level were made 10 x 10 m, the sapling level 5 x 5 m, and the seedling level 2 x 2 m. The results of the study showed that the Amahusu natural forest consists of 4 Stratifications (Strata A, B, D, and E) dominated by stratum B, where the height of stratum A trees ranges from 20-25 m while stratum B ranges from 8-19 m with dominant species including: Durian (Durio zibethinus), Canary (Canarium commune), and Linggua (Pterocarpus indicus). The composition of Amahusu forest species at the seedling level consists of 18 species, 20 species of saplings, 15 species of poles and 15 species of trees. The species diversity index is included in the medium category with an H' value at the seedling level of 1.148, saplings 1.174, poles 1.018 and trees 1.072.

Developing a Method to Estimate Above-Ground Carbon Stock of Forest Tree Species Pinus densata Using Remote Sensing and Climatic Data

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R2 and RMSE of the model based on source III were better than the R2 and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R2 by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work.

Changing role of water table and weather conditions in diameter growth of Scots pine on drained peatlands

We investigated the impact of water table (WT), monthly temperature, and precipitation of current and previous growing seasons on annual diameter growth of Scots pine in boreal drained peatlands. The data were collected from six sites across Finland. WT was monitored during 5-8 growing seasons depending on site during 2007-2014. The sites contained altogether 19 sample plots, where diameter growth from 339 trees was measured, resulting in 1,599 growth observations. Tree-level diameter growth was analysed using mixed linear models, including variables describing tree size, competition, and environment. The higher precipitation in June of the previous year and May of the current year increased diameter growth, whereas the higher temperature in July of the current year and the deeper WT in August of the previous year decreased growth. The results suggest that Scots pine grows better at shallow than deep WT in drained peatlands. This contradicts earlier findings that a deep WT is needed to support tree growth in drained peatlands. We suggest that the development of a mor layer on the peat is changing nutrient cycling and hydrology. The results encourage the avoidance of intensive drainage in forested peatlands, which may also diminish the adverse environmental impacts of peatland forestry.

The effect of ontogeny, competition, site, and climate on background mortality for trees of nine species in Canadian boreal forest.

Background tree mortality can be defined as the death of trees that naturally occurs as stands develop, in the absence of major or sudden stand disturbances. The phenomenon is often linked to ontogeny and competition and generally affects individual trees, unlike catastrophic mortality, which affects most trees in the stand. To forecast stand characteristics and to estimate how stand development could change in response to changing climate, it is necessary to quantify background mortality and to identify the most important factors involved. Using data from 10,045 permanent sample plots, we modeled background tree mortality for the 9 most abundant tree species of the eastern Canadian boreal forest. We used explanatory variables related to stand and tree ontogeny, competition, site characteristics and climate to calibrate the models. We found that an increase in age, competition and the presence of partial cut increased the mortality risk. However, the effect of DBH and site-related variables varied among species. We also found that higher temperatures, less precipitation, and higher aridity index values increased background tree mortality. According to mortality simulations under different future climate scenarios, background tree mortality could increase in the next decades for 6 of the 9 tree species studied.

Pinus densiflora var. ussuriensis Forest Alliance

The Pinus densiflora var. ussuriensis forest is a unique pine forest type in Northeast China. The species grows normally under extremely adverse conditions because it has a strong tolerance to poor soils and drought. It also benefits the ecological environment significantly through water conservation, soil improvement and windbreak and sand fixation, thus making it an important pioneer species for improving site conditions. This paper systematically analyzes the geographical distribution, ecological and community characteristics, species composition and classification of Pinus densiflora var. ussuriensis forests in China through field surveys and literature review. The main findings are: (1) Based on the dominant species of each layer, Pinus densiflora var. ussuriensis forests can be divided into two categories of associations: Pinus densiflora var. ussuriensis - Rhododendron dauricum - Carex callitrichos association and Pinus densiflora var. ussuriensis + Quercus mongolica - Lespedeza bicolor - Carex caespitosa association. (2) According to sample plot surveys, a total of 134 vascular plant species, belonging to 59 families and 104 genera, were recorded in natural Pinus densiflora var. ussuriensis forests. Amongst them were one fern species, two gymnosperm species and 131 angiosperm species. (3) The composition of life forms in Pinus densiflora var. ussuriensis forests is dominated by terrestrial herbs, with fewer woody plants and with vines making up the smallest proportion. (4) The Pinus densiflora var. ussuriensis forests located on the hillocks of Xingkai Lake are in decline. If the soil in this area continues to increase in wetness, this community may eventually evolve into a broad-leaved mixed forest.

Transformation of Forest Phytocenoses Near the Gypsum Mining Enterprise in the Arkhangelsk Region

The results of monitoring studies of forest vegetation in the vicinity of a gypsum mining enterprise in the Arkhangelsk region are presented. The study of the most important morphometric parameters and vitality of plants of all main tiers of forest communities, as well as extra-tier biota, which included epiphytic and epixylous mosses and lichens, was carried out. It was shown that the composition and structure of the forest stand, herbaceous-shrub, moss-lichen tiers, as well as extra-tier biota did not undergo noticeable changes. It was noted that on the majority of sample plots, which are located in close proximity to the quarry boundary, there was an increase in the number of undergrowth, changes in the height and vitality of undergrowth bushes.

Carbon Sequestration Assessment Using Satellite Data and GIS at Chiang Mai Rajabhat University

This study conducted a project to assess carbon sequestration in the forest area of Chiang Mai Rajabhat University, Mae Rim Campus, covering a total area of approximately 5,600 rai, with about 75% consisting of dry dipterocarp forest. The Sentinel-2 satellite data from 2019 to 2023 were used to analyze and classify forest density using the Normalized Difference Vegetation Index (NDVI). It was classified into four NDVI levels: highest, high, moderate, and low. Then, eight sample plots were distributed across all density levels to collect field data on tree species, number of trees, height, and diameter. The biomass and carbon sequestration in the sample plots showed a strong correlation with vegetation density, with the highest average correlation in February, particularly on February 13, 2023, showing the highest correlation coefficient of 0.817. This relationship is described by the equation y=78.601x‒25.726, indicating that this model is effective for estimating carbon sequestration. The analysis revealed that the area with the highest NDVI level of dry dipterocarp forest had the highest above-ground carbon sequestration rate of 16.25 tons per rai, whereas the forest with the lowest NDVI level had an above-ground carbon sequestration rate of 0.21 tons per rai. In total, the above-ground carbon sequestration for the trees amounted to 50,907.35 tons. This preliminary assessment serves as a promising foundation for future efforts the conservation and restoration of the university’s forest area, contributing to sustainable strategies for mitigating global warming.

Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data

Abstract While remote sensing can be an effective tool in building a forest inventory, field measurements and model fitting can be both expensive and challenging. One strategy to reduce forest inventory costs is to leverage forest inventory models fitted to a different population (external models), although the effectiveness of external models is poorly understood. One concern is that models may predict well to the sample data, but poorly to the population—which is termed ‘overfitting’. The effect of overfit may be especially problematic in attempts to predict for a different population (a forest area not covered by any sample plots). Assessing overfit is difficult and its consequence for estimation are not well understood, especially in the context of prediction using external models. This study assesses how overfitting affects model-assisted forest inventory estimation when using internal and external models. We used field and remotely sensed data (Sentinel-2 images and airborne laser scanning data) from two forest areas in Finland. We evaluated four modeling approaches: ordinary least square regression (OLS), random forest, k-nearest neighbors, and gaussian process regression. Both analytical and bootstrap variance estimators were used to evaluate model-assisted estimation performance. Internal models, especially OLS, were the most affected by model overfitting, leading to bias in the population means and underestimation of variance. Estimates using external models provided unbiased means and realistic intervals except in the case of deliberate excessive overfitting. The bootstrap variance estimator was found to be more robust to overfit than the analytical variance estimator for the internal model, but was not helpful for the external model. Internal models should be parsimonious to generalize well to the population and avoid bias. The bootstrap estimator of variance is recommended for internal models, especially if there is concern about model overfitting.

Tree species diversity and spatial distribution of carbon stock in forests under different management regimes in Nepal's Western Terai Arc Landscape

Reliable tree species diversity and carbon stock data are crucial for establishing a baseline and aiding monitoring and management decision-making to ensure ecosystem functions and services, including carbon management. However, such data are not available for many primary forests, especially in developing countries like Nepal. For this study, we generated primary baseline data on tree species diversity, biomass, and carbon stock for forests under different management regimes in one of the critical landscapes of western Nepal. We sampled 94 clusters of concentric circular sample plots placed systematically at the nodes of 3 km by 3 km square grids in biological corridors (managed by community forest user groups) and a national park's buffer zones (managed by buffer zone user groups) to study the tree species composition and estimate the carbon stock. The number of tree species and botanical families reported in different sites did not differ significantly. The basal area was found to be highest in Brahmadev Corridor where tree density per unit area was also highest. Our study sites showed a basal area ranging from 26.42 to 40.65 m2/ha, which is higher than previously reported from similar forests. The total biomass (302.12 to 496 tons/ha) and carbon stock (142 to 233.12 tons/ha), both being highest in Brahmadev Corridor, were within the range of comparable data and national averages. The number of stems in all sites showed the reverse J-shaped pattern, indicating a stable population and good species recruitment. The buffer zones had a large proportion of forest area with the highest carbon stock classes of 300–400 tons/ha. The basal area and diameter at breast height showed a significant positive correlation with carbon stock, indicating that the basal area is a key determinant of carbon stock. Our study provides disaggregated information on tree species diversity, the quality of forests in terms of stem density and size-class distribution, and carbon stock and its distribution in different carbon stock classes in two forest management regimes. In this sense, our study is the first of its kind in providing important insights into forest and carbon management because areas with different carbon stocks need different management prescriptions.

Diversity Patterns of Bacteria in the Root Zone of Davidia involucrata Along an Altitudinal Gradient

Davidia involucrata has an ancient origin, representing a remnant from the paleotropical flora that thrived during the Tertiary period. Altitudinal gradient acts as a natural testing ground for studying climate change, and research on the distribution patterns of microorganisms along altitudinal gradients is crucial in understanding the adaptability of D. involucrata to climate change. In our study, we examined sample sites ranging from 1600 to 2200 m in elevation, which are part of the primary habitat zone for Davidia involucrata within the Xuebaoding National Nature Reserve. In 2021, field surveys were conducted across four altitudinal gradients (1600 m, 1800 m, 2000 m and 2200 m) of the D. involucrata distribution in the nature reserve. The sampling plots were set in each altitudinal gradient, and three representative and healthy mature trees were selected as sample trees for each plot. Rhizosphere soils were used to test the soil stoichiometry characteristics and root zone microbial communities. Our findings indicated pronounced differences in soil total carbon (TC) and total phosphorus (TP) content and C:P and N:P ratios between the four altitude sites (p < 0.05). Analysis of the bacterial communities revealed higher richness (PD and Chao1 indexes) at ASL2000 and ASL2200 (high altitude) compared to ASL1600 and ASL2000 (low altitude) (p < 0.05). Non-metric multidimensional scaling analysis demonstrated a distinct clustering of bacterial communities between the high and low altitudes (p < 0.01). At the phylum level, Proteobacteria and Acidobacteria were predominant at high altitudes, while Actinobacteria and Chloroflexi dominated at low altitudes. The core microbiome, shared among all altitudes, comprised 377 genes. The analysis of differential abundance revealed notable disparities in the prevalence of certain bacterial genera with altitude, with Arthrobacter and Acidothermus experiencing the most pronounced shifts (p < 0.05). This confirmed that environmental factors significantly influenced bacterial community structure and abundance. Spearman’s rank correlations revealed that both Chao1 and PD indices were positively correlated with elevation, TC, and TN, with Chao1 showing stronger relationships. Both indices were negatively correlated with MAT, while only Chao1 exhibited a significant negative correlation with pH. Linear regression analysis further confirmed the significant associations between Chao1 index and elevation, TN, MAT, and pH. Furthermore, redundancy analysis demonstrated that altitude (ASL) and TN were the primary factors shaping soil bacterial community composition, explaining 21.32% and 30.70% of the variance, respectively. Altitude significantly influenced microbial community structure (p = 0.003). Distinct microbial taxa showed specific associations with environmental gradients, suggesting niche specialization in response to soil conditions. These findings suggest that altitude influences soil nutrient characteristics and microbial community composition in the D. involucrata habitat, offering insights into the ecological factors affecting this endangered species.

Impacts of Bedrocks on Vegetation Carbons in Typical Karst Areas: A Case Study in Puding County, Southwest China

An accurate estimation of vegetation carbon pools and their carbon sequestration potential is significant in global carbon cycle research but the existing estimations are still insufficient and largely uncertain. Here, we estimated the vegetation carbon density, carbon stocks, and carbon sequestration potential under three main bedrock types (limestone, dolomite, and non-carbonate) in Puding County, Guizhou Province, Southwestern China. The data used here included high-resolution vegetation maps of Puding, data from 274 sample plots, and the carbon contents measured previously in adjacent areas. The land area ratio of natural vegetation at an early stage (namely, grassland and shrub, excluding artificial forests and cultivated land) in carbonate rock areas is significantly larger than that in non-carbonate areas. The average existing carbon densities of vegetation in the non-carbonate, limestone, and dolomite areas were 31.59 ± 7.43, 16.75 ± 4.12, and 8.26 ± 2.45 Mg·ha−1, respectively, while their existing carbon stocks were 752.37 ± 172.85, 855.69 ± 210.65, and 208.49 ± 61.82 Gg, respectively. The maximum vegetation carbon densities of mature forests in the three bedrock types were 156.49 ± 12.92, 130.27 ± 6.05, and 117.41 ± 30.03 Mg·ha−1, respectively. Then, their average vegetation carbon sequestration potentials were 56.07 ± 23.06, 70.13 ± 11.39, and 59.11 ± 33.00 Mg·ha−1, respectively. In other words, vegetation carbon stocks in the non-carbonate, limestone, and dolomite areas increased by 1.34 ± 0.42, 3.58 ± 0.48, and 1.49 ± 0.51 Tg, respectively, after continuous evolution to mature forests. In conclusion, the potential growth of carbon density for karst vegetation is slightly higher than that of non-karst vegetation, despite its lower existing carbon density. Additionally, natural vegetation has a greater potential for carbon sequestration than plantations on all three bedrock types.

Modelling Differently Defined Dominant Stand Diameters of Monospecific Forest Plantations

Quadratic mean diameter is a widely used stand parameter present in the stand inventory summaries, while the top stand diameter is rarely reported in the literature, mainly in relation to dominant stand height. Since the dominant stand height is usually determined from the tree height-diameter curve of the stand, it is important how the top tree assemblage, used to estimate dominant diameter, is defined. The main objective of our study was to assess the bias between differently defined dominant diameter estimates for monospecific plantations of various species, to model the dominant diameter as a function of quadratic mean diameter and other relevant stand variables, and to estimate its goodness-of-fit in predicting dominant diameter and dominant height.We used data records gathered in sample plots in monospecific plantations of four tree species: Scots pine, Black pine, black locust and hybrid black poplar. We calculated the quadratic and arithmetic mean diameters of the 20% thickest trees in the plots, and the quadratic and arithmetic mean diameters of the trees, whose number corresponded to the 100 thickest trees per hectare. For each dataset, we analyzed the range and the distribution of the relative deviations calculated for each pair of dominant diameter estimates. For the Black pine plantations, regression models were developed for the two dominant diameter definitions, whose values differed most. Their goodness-of-fit was assessed from model efficiency and error statistics. The same model derivation procedure, applied to the Scots pine data, was followed by substitution of the predicted dominant diameter into a height-diameter model to assess the goodness-of-fit of the dominant height predictions.The differences between the arithmetic and quadratic means, estimated from the same subsample of trees, did not exceed 2% in all cases. However, dominant stand diameters calculated as averages of differently defined largest tree collectives differed by as much as 35%. Regardless of its definition, the dominant stand diameter was adequately predicted by a function of the quadratic mean diameter alone or considering stand basal area as a second predictor. The models showed very good accuracy of model efficiency above 0.92, average absolute error below 8%, with 90% of the relative errors less than 15%. The predicted dominant diameter value can be used in a height-diameter model to estimate with confidence the dominant stand height of a monospecific forest plantation, allowing the forecast of the stand attributes based on dominant trees when only average stand variables are known.

Assessment of diversity of plant species, composition, structure and regeneration status in Gondar Zuria District, Ethiopia

This study assesses the composition, arrangement, and state of regeneration in Gondar Zuria District. The two transect lines over the gradient were set up using a systematic random sample strategy in order to gather the number of species and their diameter for tree species in DBH (cm). The distance between the sample plots was 50 m, and the transect lines were 50 m apart. 12 circular plots were used in the total sample area of the botanical garden, and the sizes of the circular plots were 6.5 m for trees, 4.5 m for saplings, and 2.5 m for seedlings. Using the important value index and the diameter at breast height, the pattern of plant structure displays an inverted J shape. A review of the botanical garden’s plant species’ regeneration status revealed that, of the tree and shrub species, 6.25% had fair regeneration status and 25% had poor regeneration quality. While 56.25% of plant species that were only available as saplings or seedlings were regarded as “new” at the botanical garden, a total of 25% of plant species were not regenerating at all. At the study site, the species diversity as determined by the Shannon-Weiner (H’) index was 1.58. But this value shows the species diversity is included in the low biodiversity range unless they are well managed from disturbance. The species composition of the site shows a few species; only 16 plant species were found. A high number of individual species are found in the Leguminosae family.

Evaluating the impacts of area closure on soil properties in south central highland of Ethiopia

Soil degradation affects its quality and productivity of the land. To control soil degradation, various soil and water conservation (SWC) measures, including area closure are implemented. Understanding the impacts of SWC measures could support planning and implementation of land management practices. This study aimed to investigate impacts of area closure on soil properties in south central highland of Ethiopia. To acquire the necessary data, we conducted interviews with 80 households. Furthermore, nine years old area closure with physical soil and water conservation (AC-SWC), open grazing with physical SWC (OG-SWC), and open grazing without physical SWC (OG) were identified and each divided into upper, middle, and lower slope positions. In each slope position, 3 sample plots were established at 25–50 m intervals. A total of 27 composite samples and other 27 core samples for bulk density (3 treatments∗3 slope position∗3 replications) were collected and analyzed for soil properties. The data were analyzed using descriptive statistics and one-way ANOVA. The results showed that about 98 % of the respondents perceived that area closure would be an effective way to restore degraded land. Majority of the farmers (86 %) believed that area closure could reduce soil erosion and replenish soil fertility and thus, is beneficial in improving land productivity. Soil test results revealed that AC-SWC had significantly greater (P < 0.05) clay fractions, pH, available potassium, and available phosphorus than OG-SWC and OG. Variations of soil organic carbon (SOC) and total nitrogen (TN) were more pronounced. The SOC in AC-SWC was 83 % and 314 % greater than OG-SWC and OG, respectively. In the AC-SWC, TN was three and seven times higher than that of OG-SWC and OG, respectively. This could be due to vegetation restoration that reduces erosion, and enhances organic matter, moisture, and nutrient. The SOC, TN, pH, available potassium, and clay were significantly greater (P < 0.05) at lower slope position than upper in all forms of land management, which could be due to deposition at downslope. About 12 % and 11 % variations of clay fractions and SOC were, respectively, observed in between upper and lower slope positions at AC-SWC, but they varied by 25 % and 57 %, respectively, in OG. These imply area closure has had a beneficial effect on soil properties even on sloping land. AC-SWC is an important option to integrate in land management for rehabilitation of degraded soil.

Quantifying changes in forest structural complexity using bi-temporal airborne laser scanning

ABSTRACT Structural complexity is an important forest characteristic for habitat assessment, forest management and planning. However, monitoring how forest structural complexity evolves over time in various forest types has not been widely explored. In this study we investigate the feasibility of bi-temporal low-density airborne laser scanning (ALS) for the assessment of changes in light availability conditions within forest canopy, considered to imply changes in forest structural complexity. We used ALS data acquired in 2012 and 2019 to generate canopy vertical profiles by slicing the point clouds into 4 × 4 ×1 m voxels which were then rasterized and reclassified into four light availability categories. To understand structural development over time in different forest types we used field-measured tree heights and tree species information to stratify sample plots in different stand complexity categories. Stands with higher structural complexity represented increased proportions of space occupied by vegetation as well as decreased proportions of empty space below the canopy. The experiments showed the ability of low-density ALS to characterize the dynamics in canopy layering structure, implying changes in forest structural complexity. The presented methodology could potentially be upscaled and applied in the landscape-level monitoring of the development of boreal forest structural characteristics.

CPH-Fmnet: An Optimized Deep Learning Model for Multi-View Stereo and Parameter Extraction in Complex Forest Scenes

The three-dimensional reconstruction of forests is crucial in remote sensing technology, ecological monitoring, and forestry management, as it yields precise forest structure and tree parameters, providing essential data support for forest resource management, evaluation, and sustainable development. Nevertheless, forest 3D reconstruction now encounters obstacles including higher equipment costs, reduced data collection efficiency, and complex data processing. This work introduces a unique deep learning model, CPH-Fmnet, designed to enhance the accuracy and efficiency of 3D reconstruction in intricate forest environments. CPH-Fmnet enhances the FPN Encoder-Decoder Architecture by meticulously incorporating the Channel Attention Mechanism (CA), Path Aggregation Module (PA), and High-Level Feature Selection Module (HFS), alongside the integration of the pre-trained Vision Transformer (ViT), thereby significantly improving the model’s global feature extraction and local detail reconstruction abilities. We selected three representative sample plots in Haidian District, Beijing, China, as the study area and took forest stand sequence photos with an iPhone for the research. Comparative experiments with the conventional SfM + MVS and MVSFormer models, along with comprehensive parameter extraction and ablation studies, substantiated the enhanced efficacy of the proposed CPH-Fmnet model in addressing difficult circumstances such as intricate occlusions, poorly textured areas, and variations in lighting. The test results show that the model does better on a number of evaluation criteria. It has an RMSE of 1.353, an MAE of only 5.1%, an r value of 1.190, and a forest reconstruction rate of 100%, all of which are better than current methods. Furthermore, the model produced a more compact and precise 3D point cloud while accurately determining the properties of the forest trees. The findings indicate that CPH-Fmnet offers an innovative approach for forest resource management and ecological monitoring, characterized by cheap cost, high accuracy, and high efficiency.

Does It Matter Whether to Use Circular or Square Plots in Forest Inventories? A Multivariate Comparison

The design of a sampling unit, whether a simple plot or a subplot within a clustered structure, including shape and size, has received little attention in inferential forestry research. The use of auxiliary variables from remote sensing impacts the precision of estimators from both model-assisted and model-based inference perspectives. In both cases, model parameters are estimated from a sample of field plots and information from pixels corresponding to these units. In studies assisted by remote sensing, the shape of the plot used to fit regression models (typically circular) often differs from the shape of the population elements for prediction, where the area of interest is divided into equal tessellated parts. This raises interest in understanding the effect of the sampling unit shape on the mean of variables in forest stands of interest. Therefore, the objective of this study was to evaluate the effect of circular and square subplots, concentrically overlapped and arranged in an inverted Y cluster structure, over tree density, basal area, and aboveground biomass in a managed temperate forest in central Mexico. We used a Multivariate Generalised Linear Mixed Model, which considers the Gamma distribution of the variables and accounts for spatial correlation between Secondary Sampling Units nested within the Primary Sampling Unit. The main findings of this study indicate that the type of secondary sampling unit of the same area and centroid, whether circular or square, does not significantly affect the mean tree density (trees), basal area (m2), and aerial biomass.

Aboveground Carbon Stock Estimation Based on Backpack LiDAR and UAV Multispectral Imagery at the Forest Sample Plot Scale

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing’an larch R2 = 0.95, RMSE = 3.99; white birch R2 = 0.96, RMSE = 3.45) than multiple linear regression models (Xing’an larch R2 = 0.92, RMSE = 6.15; white birch R2 = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing’an larch R2 = 0.95, white birch R2 = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing’an larch R2 = 0.93, white birch R2 = 0.94).

Elevation, Soil and Environmental Factors Determine the Spatial and Quantitative Distribution of Qinghai Spruce Recruitment Biomass in Mountainous (Alpine) Watersheds

Soil heterogeneity observed in the alpine environment plays a very important role in the growth of forest recruitment. However, the mechanisms by which the biomass accumulation and allocation patterns of forest recruitment respond to such environmental differences are unclear, which hinders a thorough understanding of climate change’s impact on forest biomass. We hypothesized that soil heterogeneity influences the distribution of Qinghai spruce recruitment biomass along with elevation. In the frame of this study, carried out in the northern Tibetan Plateau, forest Qinghai spruce recruitment data were combined with soil data derived from 24 sample plots, while permutation multifactor ANOVA and multiple linear regression were utilized to reveal the characteristics of forest recruits’ above- and below-ground biomass and their allocation patterns in response to soil heterogeneity. According to the results, the soil heterogeneity mainly affected the distribution characteristics of recruits’ above- and below-ground biomass at different elevations, while the recruits’ root–shoot ratio variability was influenced by a combination of soil and other environmental factors. Soil organic carbon (SOC) had the greatest effect on the variability of the above- and below-ground biomass of spruce recruits, with R2 of 0.280 and 0.257, respectively. Soil organic carbon and soil moisture content (SMC) had a significant effect on the variability of the root–shoot ratio, with R2 of 0.168 and 0.165, respectively. Soil total nitrogen (TN) and soil organic carbon were the main influencing factors of the above-ground biomass of forest recruits, with contribution rates of 43.15% and 35.28%, respectively. Soil total nitrogen and soil organic carbon were also the main factors influencing the below-ground biomass of forest recruits, with contribution rates of 42.52% and 37.24%, respectively, and both of them had a positive effect on biomass accumulation, and the magnitude of the influence varied with the elevation gradient. Soil moisture content was the main influence factor of spruce recruits’ root–shoot ratio, with a contribution rate of 54.12%. Decreasing soil moisture content would significantly increase the root–shoot ratio of spruce recruits and promote plants to allocate more biomass to root growth. Changes in elevation not only affected the intensity of the effect of soil factors on spruce recruitment biomass and its allocation pattern but even led to a change in the positive and negative effects.

Mycolichenological portrait of alder forest: alpha diversity revealed in 1ha plot in the Samursky National Park (Republic of Dagestan, Russia)

Aim. Xylobiont fungi and lichens determine the sustainability of forest ecosystems, but their species richness and taxonomic diversity depend on the forest type and the degree of its disturbance. Alder forests, characterised by a specific set of ecological conditions, represent poorly studied habitats in terms of myco‐ and lichenobiota. The aim of this work was to determine the species composition of aphyllophoroid fungi and epiphytic lichens on a 1 ha sample plot in a lowland floodplain forest dominated by Alnus glutinosa in the Delta Samura area of the Samursky National Park.The authors collected basidiomata of lignicolous fungi and lichen specimens on a sample plot of 1 ha during field studies. The surveyed forest area is represented by a community dominated by Alnus glutinosa and lianas. The material was identified using light microscopy techniques and a standard set of chemical reactions.Thirty‐three species of aphyllophoroid fungi (Basidiomycota) and 53 species of lichens and allied fungi (Ascomycota) were identified. Among them, 16 species (48 %) of aphyllophoroid fungi were recorded for the first time for the Samursky National Park, including six species revealed for the first time for the Republic of Dagestan, of which three species (Donkia pulcherrima, Phanerochaete cumulodentata and Sertulicium granuliferum) were new to the Northern Caucasus. Lichen species Arthothelium ruanum, Bacidia arceutina and Graphis pulverulenta are listed for the first time for the Republic of Dagestan. At the same time, 21 species of lichenized fungi were recorded for the first time on Alnus glutinosa within the region.The taxonomical structure of aphyllophoroid fungi revealed reflects the spring period of basidiomata‐based field study. The predominance of corticioid fungi and revealing hydnoid species among morphological groups may be indicative of the generally wetter habitats of alder forests in comparison to other forest types distributed in the studied area. Most of species were recorded on dead wood of Alnus glutinosa as the main forest‐forming tree. At the same time, each of other substrates (Carpinus betulus and Corylus avellana) turned out to be the habitat of species new to the region. The group of fungal species developing on large‐scale substrate units, represented by fallen trunks, was the most prevalent and is to be considered as more vulnarable in a case of anthropogenical habitat disturbance. The majority of epiphytic lichens discovered on alder are not specific to this woody substrate, except for single findings. These species belong to epiphytes inhabiting the Samur forest, the core of the lichenobiota of which is composed of thermophilous lichens of lowland deciduous forests. In our opinion, the relict character of the forest, which is an isolated and rather small forest massif with relatively homogeneous climatic conditions, causes a high similarity between the species composition of lichens revealed in the surveyed sample plot with Alnus glutinosa and previously studied plots dominated by Carpinus betulus, Populus alba, and Quercus robur.