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Accurate detection and delineation of individual trees and their crowns in dense forest environments are essential for forest management and ecological applications. This study explores the potential of combining leaf-off and leaf-on structure from motion (SfM) data products from unoccupied aerial vehicles (UAVs) equipped with RGB cameras. The main objective was to develop a reliable method for precise tree stem detection and crown delineation in dense deciduous forests, demonstrated at a structurally diverse old-growth forest in the Hainich National Park, Germany. Stem positions were extracted from the leaf-off point cloud by a clustering algorithm. The accuracy of the derived stem co-ordinates and the overall UAV-SfM point cloud were assessed separately, considering different tree types. Extracted tree stems were used as markers for individual tree crown delineation (ITCD) through a region growing algorithm on the leaf-on data. Stem positioning showed high precision values (0.867). Including leaf-off stem positions enhanced the crown delineation, but crown delineations in dense forest canopies remain challenging. Both the number of stems and crowns were underestimated, suggesting that the number of overstory trees in dense forests tends to be higher than commonly estimated in remote sensing approaches. In general, UAV-SfM point clouds prove to be a cost-effective and accurate alternative to LiDAR data for tree stem detection. The combined datasets provide valuable insights into forest structure, enabling a more comprehensive understanding of the canopy, stems, and forest floor, thus facilitating more reliable forest parameter extraction. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the effects of altitude, the position of the trees along a gradient of canopy cover, and the orientation of their crown on leaf traits of 18 deciduous woody species belonging to 10 families in the Fandoglo forest region in Ardabil, North West of Iran. We measured eight leaf traits (leaf width, length, area, thickness, water content, leaf mass per area, specific leaf area, and dry matter concentration) of trees sampled at sites subjected to different light regimes (forest edge, forest understory, and isolated trees). All traits were measured on more than 3600 leaves from 90 trees sampled in two altitudinal ranges (low: 1300-1500 m a.s.l.; high: 1500- 1700 m a.s.l.). A two-way ANOVA and t-test for independent samples were applied to test for differences in leaf traits between different altitudes and degree of canopy cover. The results confirmed that species' leaf traits were more strongly correlated with the altitude and canopy cover rather than the orientation of the crown. No relationship between leaf traits and crown orientation was detected. All leaf traits had significantly higher values at low than at high elevation, indicating that environmental factors such as atmospheric CO2 concentration, temperature, light, irradiance, and wind deeply impact on foliar morphology and function; however, water content and specific leaf area showed an opposite trend. Also, species with different positions along the gradient of canopy cover could have different responses to elevation. Our results indicate that the variation of functional (morphological and physiological) traits in different tree species are affected by altitude and light regime. This might provide a theoretical basis for afforestation and forest management activities in the Fandoghlo forest region. [ABSTRACT FROM AUTHOR]

Reliable forest resource information is needed to assess the forest development status and design management plans for forest maintenance and conservation. The forest field sample inventory is a vital forest resource inventory method. Thus, forest inventory reliability depends on tree attribute estimation accuracy and the quantity and quality of field samples. Simultaneous localization and mapping (SLAM)-based mobile laser scanners (MLSs) are convenient inventory tools due to their mobility and global navigation satellite system (GNSS) signal independence. However, such scanners may be heavy, expensive and unable to verify results on-site. With the improved SLAM algorithm and chip computing capabilities, a smartphone can deploy an online SLAM system, which allows the smartphone to perform the relative positioning in forests without GNSS signals. Previous research studies have demonstrated this simple, portable, and economical device for estimating the tree position and diameter at breast height (DBH) through tree-by-tree measurements in real time. However, the device might face a challenge in large-scale forest inventories because the image-feature-based backend may not work well in forests that are not well constructed for traditional SLAM systems. In this paper, an online trunk-based backend was designed to accurately estimate tree position and correct pose drift in large-scale forest inventories in real time. Specifically, a trunk-based loop closure detection algorithm was designed for detecting whether an earlier observed tree is re-observed to provide nodes and constraints for tree position graph optimization; this algorithm uses the provided nodes and constraints to build and optimize the tree position graph and then correct the current pose based on the optimized globally consistent tree position graph. This new backend was integrated with the previous work as an executable program that can be deployed on a smartphone with an online RGB-D SLAM system. The method was tested in 5 field sample plots (32 × 32 m), and the reference tree positions were collected using terrestrial laser scanning (TLS) through multi-scan mode. The distance mean between the estimated and reference tree positions was 0.133 m when using our new backend, and it was 0.759 m when estimated with the RTAB-Map. The tree position estimates were unbiased and had root mean square errors (RMSEs) of less than 0.09 m in the x-axis, y-axis and z-axis directions when using the trunk-based backend. However, the estimates had biases of −0.125 m, −0.261 m and 0.262 m and RMSEs of more than 0.30 m in the x-axis, y-axis and z-axis directions without the new backend. The results showed that the designed trunk-based backend allows a smartphone with an online SLAM system to function as an accurate and efficient tool for large-scale forest inventories. However, the method was tested only in 32 × 32 m square field sample plots. More tests must be performed in larger plots in the future, although enough loop-closure constraints can theoretically guarantee the accuracy of the tree position graph and current pose. [ABSTRACT FROM AUTHOR]