Search Results (70)

The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%. Full article

The accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-range photogrammetry (CRP) is widely used in the 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, novel view synthesis (NVS) technology, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using imaging devices with different resolutions (cameras on smartphones and UAV). These plots included one with sparse, leafless trees and another with dense foliage and more occlusions. We then performed dense reconstruction of forest stands using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. The photogrammetric method is suitable for relatively simple forest stands, as it is less adaptable to complex ones. This results in tree point cloud models with issues such as excessive canopy noise and wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, yielding tree point clouds of the highest quality that offer more detailed trunk and canopy information. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings confirm that NVS methods have significant application potential for 3D reconstruction of urban forests. Full article

Soil nutrient and fertility assessments provide a direct measure for evaluating forest management effects. In this study, we examined soil nutrient content in Chinese fir (Cunninghamia lanceolata) plantations under four reconstruction patterns: pure plantation, introduced broadleaf, introduced needleleaf, and introduced mixed broadleaf-needleleaf. The soil fertility index (SFI) evaluation model was constructed based on partial least squares path modeling (PLS-PM), revealing the influence of stand characteristics on SFI in early stand reconstruction. The results showed that, compared to pure plantations, total nutrient content increased in the introduced needleleaf pattern by 13.94% to 21.15% and available nutrient content by 18.21% to 26.91%. In contrast, both introduced broadleaf and mixed broadleaf-needleleaf exhibited a declining trend. Significant differences were observed among the reconstruction patterns (p < 0.05). In the SFI evaluation model, soil chemistry total nutrient (SCT) and soil chemistry available nutrient (SCA) made significant contributions. The weights of SCT and SCA in SFI were 0.52 and 0.48, respectively. The SFI of four patterns ranged from 0.43 to 0.58, indicating relatively low soil fertility. Compared to pure plantations, introduced trees did not enhance soil fertility in early stand reconstruction. The SFI of the introduced needleleaf was significantly higher than that of the other two reconstruction patterns (p < 0.05). Stand construction (including diameter at breast height, tree density, and tree biomass) explained 14.69% of SFI variation, with a contribution of 31.72% in the surface soil layer (0~20 cm). Tree biomass significantly influenced SFI variation, accounting for over 40% of the total stand factors. Retained tree biomass had a substantially greater effect than introduced tree biomass, contributing twice as much to SFI variation. PLS-PM could effectively reflect the soil nutrient status and accurately estimate the weight of soil fertility. In early stand reconstruction, retained tree biomass might be the major influence on soil fertility variation. We suggest determining reasonable thinning intensity to retain enough Chinese fir and promote the growth of introduced trees. This study introduces a novel approach to soil fertility assessment and provides theoretical support for formulating effective forest management strategies in the early reconstruction of Chinese fir plantations. Full article

Drone-mounted LiDAR systems have revolutionized forest mapping, but data quality is often compromised by occlusions caused by vegetation and terrain features. This study presents a novel framework for analyzing and predicting LiDAR occlusion patterns in forested environments, combining the geometric reconstruction of flight paths with the statistical modeling of ground visibility. Using field data collected at Unzen Volcano, Japan, we first developed an algorithm to retrieve drone flight paths from timestamped pointclouds, enabling post-processing optimization, even when original flight data are unavailable. We then created a mathematical model to quantify the shadow effects from obstacles and implemented Monte Carlo simulations to optimize flight parameters for different forest stand characteristics. The results demonstrate that lower-altitude flights (40 m) with narrow scanning angles achieve the highest ground visibility (81%) but require more flight paths, while higher-altitude flights with wider scanning angles offer efficient coverage (47% visibility) with single flight paths. For a forest stand with 250 trees per 25 hectares (heights 5–15 m), statistical analysis showed that scanning angles above 90 degrees consistently delivered 46–47% ground visibility, regardless of the flight height. This research provides quantitative guidance for optimizing drone LiDAR surveys in forested environments, though future work is needed to incorporate canopy complexity and seasonal variations. Full article

The Eastern Baltic area stands out as a unique location due to the finds of Europe’s youngest dated mammoth remains (12.6–11.2 ka cal BP). Our study explores the drastic climate and landcover changes during the extinction of these gigantic herbivores at the Pleistocene/Holocene boundary. We used macrofossil analysis to determine the major contemporary terrestrial plant genera present in the area and used corresponding pollen taxa for REVEALS model-based landcover reconstructions. Our results indicate that these last mammoths utilised the open landcover of the Eastern Baltic, which developed as the continental ice sheet retreated during the termination of the last glaciation. Due to climate warming during the initial stages of the Holocene interglacial, the Eastern Baltic became speedily populated by birch and pine forests. The abrupt disappearance of typical forb-dominated tundra indicators, such as Dryas octopetala, and the fast increase in tree birch marked a shift from an open, tundra-like landscape to a forested one, making the environment inhospitable for mammoths even in northernmost Estonia by the beginning of the Holocene. A comparison between the isotopic values of nitrogen (δ¹⁵N) and carbon (δ¹³C) obtained from mammoths’ molars from 14.3 and 11.3 to 43.5 and 39.1 ka cal BP showed that mammoths experienced a decline in the nutritional value of their diet, resulting in their demise in the Eastern Baltic. Full article

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. 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 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

Pinus pinea is an important Mediterranean species due to its adaptability and tolerance to aridity and its high-quality pine nuts. Different forest types located in Mediterranean native and non-native environments provide the opportunity to perform comparative studies on the species’ response to climate change. The aims of this study were to elucidate growth patterns of the species growing in native and exotic habitats and to analyze its response to climatic fluctuations, particularly drought, in both geographical contexts. Understanding stone pine (Pinus pinea) growth responses to climate variability in native and exotic habitats by comparing natural stands and plantations may provide useful information to plan adequate management under climate change. By doing so, we enhance the understanding of P. pinea’s adaptability and provide practical approaches to its sustainable management. In this study, we reconstructed and compared the stem radial growth of seven stone pine stands, two in southern Spain and five in central–southern Chile, growing under different climatic conditions. We quantified the relationships between growth variability and climate variables (total rainfall, mean temperature, and SPEI drought index). Growth was positively correlated with autumn rainfall in plantations and with autumn–winter rainfall in natural stands. Growth was also enhanced by high autumn-to-spring rainfall in the driest Chilean plantation, whereas in the wettest and coolest plantation, such correlation was found in winter and summer. A negative impact of summer temperature was found only in one of the five Chilean plantations and in a Spanish site. The correlation between SPEI and tree-ring width indices showed different patterns between and within countries. Overall, exotic plantations showed lower sensitivity to climate variability than native stands. Therefore, stone pine plantations may be useful to assist in mitigating climate change. Full article

Over the past few decades, increasing populations of cervid species in the Baltic region have reduced the quality and vitality of cultivated Norway spruce (Picea abies (L.) Karst.) stands. This study evaluated the effect of bark stripping on the volume growth of spruce trees in Latvia. Data collection took place in two forest stands. In each stand, 20 Norway spruce trees were sampled, 10 with visible bark damage scars and 10 control trees. Stem discs were collected from control trees at specified heights (0 m, 0.5 m, 1 m, 1.3 m, and 2 m, and then at one-metre intervals up to the top) and from damaged trees at additional specific points relative to the damage. Each disc was sanded and scanned; tree ring widths were measured in 16 radial directions using WinDendro 2012a software. Annual volume growth reconstruction was performed for each tree. Changes in relative volume growth were analysed in interaction with scar parameters, tree type (damaged/control), and pre-damage volume using linear regression models. The significance of parameter interactions was assessed using analysis of variance (ANOVA). Pairwise comparisons of estimated marginal means (EMMs) were conducted using Tukey’s HSD post hoc test. No significant effect of bark stripping on the total stem volume increment was detected. However, the length of bark stripping scars had a significant impact on relative volume growth in the lower parts of the stems. These findings underscore the importance of further research examining a broader spectrum of cervid damage intensity and the effects of repeated damage on tree survival and growth. Full article

Stress wave technology is very suitable for detecting internal defects of standing trees, logs, and wood and has gradually become the mainstream technology in this research field. Usually, 12 sensors are positioned equidistantly around the cross-section of tree trunks in order to obtain enough stress wave signals. However, the arrangement of sensors is time-consuming and laborious, and maintaining the accuracy of stress wave imaging under sparse signals is a challenging problem. In this paper, a novel stress wave hybrid imaging method based on compressive sensing and elliptic interpolation is proposed. The spatial structure of the defective area is reconstructed by using the advantages of compressive sensing in sparse signal representation and solution of stress waves, and the healthy area is reconstructed by using the elliptic space interpolation method. Then, feature points are selected and mixed for imaging. The comparative experimental results show that the overall imaging accuracy of the proposed method reaches 89.7%, and the high-quality imaging effect can be guaranteed when the number of sensors is reduced to 10, 8, or even 6. Full article

Diameter at breast height (DBH) is a unique attribute used to characterize forest growth and development for forest management planning and to understand forest ecology. Forest managers require an array of DBHs of forest stands, which can be reconstructed using selected probability distribution functions (PDFs). However, there is a lack of practices that fit PDFs of sub-dominating species grown in natural mixed forests. This study aimed to fit PDFs and develop predictive models for PDF parameters, so that the predicted distribution would represent dynamic forest structures and compositions in mixed forest stands. We fitted three of the simplest forms of PDFs, log-normal, gamma, and Weibull, for the DBH of eight tree species, namely balsam fir (Abies balsamea [L.] Mill.), eastern white pine (Pinus strobus L.), paper birch (Betula papyrifera Marshall), red maple (Acer rubrum L.), red pine (Pinus resinosa Aiton), sugar maple (Acer saccharum Marshall), trembling aspen (Populus tremuloides Michx), and white spruce (Picea glauca [Moench] Voss), all grown in natural-origin mixed forests in Ontario province, Canada. We estimated the parameters of the PDFs as a function of DBH mean and standard deviation for these species. Our results showed that log-normal fit the best among the three PDFs. We demonstrated that the predictive model could estimate the recovered parameters unbiasedly for all species, which can be used to reconstruct the DBH distributions of these tree species. In addition to prediction, the cross-validated R² for the DBH mean ranged between 0.76 for red maple and 0.92 for red pine. However, the R² for the regression of the standard deviation ranged between 0.00 for red pine and 0.69 for sugar maple, although it produced unbiased predictions and a small mean absolute bias. As these mean and standard deviations are regressed with dynamic covariates (such as stem density and stand basal area), in addition to climate and static geographic variables, the predicted DBH distribution can reflect change over time in response to management or any type of disturbance in the regime of the given geography. The predictive model-based DBH distributions can be applied to the design of appropriate silviculture systems for forest management planning. Full article

(1) The early assessment of wood quality, even while trees are standing, provides significant benefits for forest management, sales efficiency, and market diversification. Its definition cannot be in absolute terms but must always be linked to the material’s intended use. (2) In this contribution, a review of the scientific literature is given to discuss the visually evaluable attributes that define wood quality in standing trees, the applicability of the techniques used for their assessment, and the effectiveness of these attributes and technologies in predicting quality, to finally highlight future research needs. (3) The visual characteristics generally used to evaluate wood quality are linked to stem form and dimension, branchiness, and stem damage, but their assessment is challenging due to time and resource constraints. To address these challenges, laser-based and image-based techniques have been applied in field surveys. (4) Laser scanners offer detailed and accurate measurements. Photogrammetry, utilizing images to reconstruct 3D models, provides a cost-effective and user-friendly alternative. Studies have demonstrated the effectiveness of these tools in surveying the visible properties of stems and branches, but further development is necessary for widespread application, particularly in software development, with faster and more effective algorithmic advancements for automatic recognition and subsequent measurement of pertinent characteristics being critical for enhancing tool usability. (5) However, predicting wood quality from these surveys remains challenging, with a limited correlation between the visible tree characteristics assessed and the sawn product quality. Empirical studies evaluating products downstream in the forest-wood supply chain could provide valuable insights. In this sense, the implementation of traceability systems could facilitate the linkage between data on standing trees and the quality of the sawn product. Also, further research is needed to develop models that can accurately predict internal tree characteristics and their impact on product quality. Full article

The El Cielo Biosphere Reserve (CBR) stands as a vital forested region in eastern Mexico due to its high biodiversity in flora and fauna and provision of environmental services. This study established a network of 10 ring-width chronologies of different species within the CBR and adjacent watersheds. The objective was to analyze their climatic response and reconstruct the seasonal streamflow contribution of each sub-basin to the main stream, utilizing data from a gauge network of eight hydrological stations located at strategic locations of the CBR. With chronologies ranging from 116 to 564 years, most exhibited association with the accumulated streamflow between January and June. Based on the adjusted R², Akaike Information Criteria, and Variance Inflation Factor, the stepwise regression procedure was selected among different statistical methods for developing the reconstruction model. In spite of differences in the seasonal reconstructed periods, all the species showed potential to develop hydrological reconstructions as indicated by their common response to streamflow variability, as occurred in the wet years of 1976, 1993, 2000, and 2008, and dry years of 1980, 1982, 1996, and 2011. It was found that the response of the chronologies to gauge records increased as a function of the chronologies’ interseries correlation, average mean sensitivity, and distance of the tree-ring series to the gauge station. Streamflow reconstructions at the sub-basin level allowed a better understanding of the hydroclimatic variability characterizing the CBR, but also suggested the need to increase the network of chronologies for some particular sub-basins lacking tree-ring series to improve the reconstructed models. Full article

Fraxinus excelsior L. (ash) is a key forest tree species challenged by Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz, Hosoya, the causal agent of ash dieback. The goals of this study were (I) to assess the presence, spatial distribution, and incidence of H. fraxineus in the inner-alpine valleys of northwestern Italy, along with the severity of ash dieback; (II) to model the probability of infection by H. fraxineus based on environmental variables; (III) to reconstruct the direction of provenance of the front of invasion of the pathogen; and (IV) to test whether H. fraxineus has replaced the native relative Hymenoscyphus albidus (Gillet) W. Phillips, a saprobe of ash litter. By combining phytosanitary monitoring and samplings in 20 forest stands, laboratory analyses, and statistical modelling, this study showed that H. fraxineus was present in 65% of stands with an average incidence of 27%, reaching peaks of 80%. Rainfalls were the most relevant drivers of the probability of infection by H. fraxineus, rising up to 80% with the increased precipitation in April and July. Other drivers included elevation, maximal temperatures, latitude, and longitude. The front of invasion likely moved from Italy and/or Switzerland, rather than from France, while the replacement of H. albidus is uncertain. Full article