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We compiled a data set for all tree species collected to date in lowland Amazonian Ecuador in order to determine the number of tree species in the region. This data set has been extensively verified by taxonomists and is the most comprehensive attempt to evaluate the tree diversity in one of the richest species regions of the Amazon. We used four main sources of data: mounted specimens deposited in Ecuadorian herbaria only, specimen records of a large‐scale 1‐hectare‐plot network (60 plots in total), data from the Missouri Botanical Garden Tropicos® database (MO), and literature sources. The list of 2,296 tree species names we provide in this data set is based on 47,486 herbarium records deposited in the following herbaria: Alfredo Paredes Herbarium (QAP), Catholic University Herbarium (QCA), Herbario Nacional del Ecuador (QCNE), Missouri Botanical Garden (MO), and records from an extensive sampling of 29,768 individuals with diameter at breast height (dbh) ≥10 cm recorded in our plot network. We also provide data for the relative abundance of species, geographic coordinates of specimens deposited in major herbaria around the world, whether the species is native or endemic, current hypothesis of geographic distribution, representative collections, and IUCN threat category for every species recorded to date in Amazonian Ecuador. These data are described in Metadata S1 and can be used for macroecological, evolutionary, or taxonomic studies. There are no copyright restrictions; data are freely available for noncommercial scientific use (CC BY 3.0). Please see Metadata S1 (Class III, Section B.1: Proprietary restrictions) for additional information on usage.

We present repeated tree measurement data from 63 permanent plots in mountain forests in France. Plot elevations range from 800 (lower limit of the montane belt) to 1942 m a.s.l (subalpine belt). Forests mainly consist of pure or mixed stands dominated by European beech (Fagus sylvatica), Silver fir (Abies alba) and Norway spruce (Picea abies), in association with various broadleaved species at low elevation and with Arolla pine (Pinus cembra) at high elevation. The plot network includes 23 plots in stands that have not been managed for the last 40 years (at least) and 40 plots in plots managed according to an uneven-aged system with single-tree or small-group selection cutting. Plot sizes range from 0.2 ha to 1.9 ha. Plots were installed from 1994 to 2004 and re-measured 2 to 5 times during the 1994-2015 period. During the first census (installation), living trees more than 7.5 cm in dbh were identified, their diameter at breast height (dbh) was measured and their social status (strata) noted. Trees were spatially located, either with x, y and z coordinates (40 plots) or within subplots 0.25 ha square (23 plots). In addition, in a subset of plots (58 plots), tree heights and tree crown dimensions were measured on a subset of trees and dead standing trees and stumps were included in the census. Remeasurements after installation include live tree diameters (including recruited trees), tree status (living, damaged, dead, stump), and for a subset of trees, height. At the time of establishment of the plots, plot densities ranges from 181 to 1328 stemsha(-1) and plot basal areas ranges from 13.6 to 81.3 m(2) ha(-1).

Obtaining the geographic coordinates of single fruit trees enables the variable rate application of agricultural production materials according to the growth differences of trees, which is of great significance to the precision management of citrus orchards. The traditional method of detecting and positioning fruit trees manually is time-consuming, labor-intensive, and inefficient. In order to obtain high-precision geographic coordinates of trees in a citrus orchard, this study proposes a method for citrus tree identification and coordinate extraction based on UAV remote sensing imagery and coordinate transformation. A high-precision orthophoto map of a citrus orchard was drawn from UAV remote sensing images. The YOLOv5 model was subsequently used to train the remote sensing dataset to efficiently identify the fruit trees and extract tree pixel coordinates from the orchard orthophoto map. According to the geographic information contained in the orthophoto map, the pixel coordinates were converted to UTM coordinates and the WGS84 coordinates of citrus trees were obtained using Gauss–Krüger inverse calculation. To simplify the coordinate conversion process and to improve the coordinate conversion efficiency, a coordinate conversion app was also developed to automatically implement the batch conversion of pixel coordinates to UTM coordinates and WGS84 coordinates. Results show that the Precision, Recall, and F1 Score for Scene 1 (after weeding) reach 0.89, 0.97, and 0.92, respectively; the Precision, Recall, and F1 Score for Scene 2 (before weeding) reach 0.91, 0.90 and 0.91, respectively. The accuracy of the orthophoto map generated using UAV remote sensing images is 0.15 m. The accuracy of converting pixel coordinates to UTM coordinates by the coordinate conversion app is reliable, and the accuracy of converting UTM coordinates to WGS84 coordinates is 0.01 m. The proposed method is capable of automatically obtaining the WGS84 coordinates of citrus trees with high precision.

Decision tree algorithms, such as random forest, have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit spatial trends into these algorithms has proven problematic. Using x and y coordinates as covariates gives orthogonal artifacts in the maps, and alternative methods using distances as covariates can be inflexible and difficult to interpret. We propose instead the use of coordinates along several axes tilted at oblique angles to provide an easily interpretable method for obtaining a realistic prediction surface. We test the method on four spatial datasets and compare it to similar methods. The results show that the method provides accuracies better than or on par with the most reliable alternative methods, namely kriging and distance-based covariates. Furthermore, the proposed method is highly flexible, scalable and easily interpretable. This makes it a promising tool for mapping soil properties with complex spatial variation.

The tree species composition can influence the dynamics of herbaceous species and enhance the spatial heterogeneity of the soil. But there is very little evidence on how both overstorey structure and soil properties affect the spatial variation of the herb layer. The aim of this study is to evaluate the factors of the soil and overstorey structure by which it is possible to explain the fine-scale variation of herbaceous layer communities in an Eastern European poplar-willow forest. The research was conducted in the “Dnipro-Orils’kiy” Nature Reserve (Ukraine). The research polygon (48°30′51″N, 34°49″02″E) was laid in an Eastern European poplar-willow forest in the floodplain of the River Protich, which is a left inflow of the River Dnipro. The site consists of 7 transects. Each transect was made up of 15 test points. The distance between rows in the site was 3 m. At the site, we established a plot of 45×21 m, with 105 subplots of 3×3 m organized in a regular grid. The adjacent subplots were in close proximity. Vascular plant species lists were recorded at each 3×3 m subplot along with visual estimates of species cover using the nine-degree Braun-Blanquet scale. Within the plot, all woody stems ≥ 1 cm in diameter at breast height were measured and mapped. Dixon’s segregation index was calculated for tree species to quantify their relative spatial mixing. Based on geobotanical descriptions, a phytoindicative assessment of environmental factors according to the Didukh scale was made. The redundancy analysis was used for the analysis of variance in the herbaceous layer species composition. The geographic coordinates of sampling locations were used to generate a set of orthogonal eigenvector-based spatial variables. Two measurements of the overstorey spatial structure were applied: the distances from the nearest tree of each species and the distance based on the evaluation of spatial density of point objects, which are separate trees. In both cases, the distance matrix of sampling locations was calculated, which provided the opportunity to generate eigenvector-based spatial variables. A kernel smoothed intensity function was used to compute the density of the trees’ spatial distribution from the point patterns’ data. Gaussian kernel functions with various bandwidths were used. The coordinates of sampling locations in the space obtained after the conversion of the trees’ spatial distribution densities were used to generate a set of orthogonal eigenvector-based spatial variables, each of them representing a pattern of particular scale within the extent of the bandwidth area structured according to distance and reciprocal placement of the trees. An overall test of random labelling reveals the total nonrandom distribution of the tree stems within the site. The unexplained variation consists of 43.8%. The variation explained solely by soil variables is equal to 15.5%, while the variation explained both by spatial and soil variables is 18.0%. The measure of the overstorey spatial structure, which is based on the evaluation of its density enables us to obtain different estimations depending on the bandwidth. The bandwidth affects the explanatory capacity of the tree stand. A considerable part of the plant community variation explained by soil factors was spatially structured. The orthogonal eigenvector-based spatial variables (dbMEMs) approach can be extended to quantifying the effect of forest structures on the herbaceous layer community. The measure of the overstorey spatial structure, which is based on the evaluation of its density, was very useful in explaining herbaceous layer community variation.

In order to ensure canopy area coverage with the most compact mechanical configuration possible, this paper proposes a configuration optimization design method of dual-manipulator to meet the research and development needs of an apple-efficient harvesting robot using the typical tree shape of a “high spindle” in China as the object. A Cartesian coordinate dual-manipulator with two groups of vertically synchronous operations and a three-degree range of motion based on the features of the spatial distribution of fruits under a typical canopy of dwarf and close planting was designed. Two-stage telescoping components that can be driven by both gas and electricity are employed to ensure the picking robotic arm’s quick response and accessibility to the tree crown. Based on the quantitative description of the working space and configuration parameters of the dual-manipulator, a multi-objective optimization model of the major configuration parameters is constructed. A comprehensive evaluation method of the dual-manipulator configuration based on the CRITIC–TOPSIS combined method is proposed. The optimal solutions of the lengths and elevations of upper and lower telescopic parts of the dual-manipulator and the distance from the mounting base of the outer frame of the dual-manipulator to the center of the tree trunk are determined, which are 1119.3 mm and 39.4°, 898.7 mm and 26°, 755.3 mm, respectively. The interaction between the configuration parameters of the dual-manipulator and its working area is then simulated and examined in order to verify the rationality of the optimum configuration settings. The results show that the optimal configuration of the dual-manipulator can fully cover the target working space, and the redundancy rate is 16.62%. The results of this study can be utilized to advance robotic fruit-picking research and development.

This paper proposes tools for spatial performance analysis in basketball. In detail, we aim at representing maps of the court visualizing areas with different levels of scoring probability of the analysed player or team. To do that, we propose the adoption of algorithmic modeling techniques. Firstly, following previous studies, we examine CART, highlighting strengths and weaknesses. With respect to what done in the past, here we propose the use of polar coordinates, which are more consistent with the basketball court geometry. In order to overcome CART’s drawbacks while maintaining its points of force, we propose to resort to CART-based ensemble learning algorithms, namely to Random Forest and Extremely Randomized Trees, which are shown to be able to give excellent results in terms of interpretation and robustness. Finally, an index is defined in order to measure the map’s graphical goodness, which can be used—jointly with measures of the out-of-sample error—to tune the algorithm’s parameters. The functioning of the proposed approaches is shown by the analysis of real data of the NBA regular season 2020/2021.

Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a spatial hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance.

Forest gaps play an important role during forest succession in temperate forest ecosystems. However, the differences in spatial distribution and replacement patterns of woody plants (trees and shrubs) between primary and secondary forests remain unclear during the gap-filling processes, especially for temperate forests in Northeast China. We recorded 45,619 regenerated trees and shrubs in young gaps (<10 years), old gaps (10~20 years), and closed forest stands (i.e., filled gaps) in the primary broadleaved Korean pine ( Pinus koraiensis Sieb. Rt Zucc.) forests vs. secondary forests (degraded from primary forests). The gap-filling processes along horizontal (Cartesian coordinate system) and vertical (lower layer: 0~5 m, medium layer: 5~10 m, and upper layer: >10 m) dimensions were quantified by shade tolerance groups of trees and shrubs. We found that gap age, competition between species, and pre-existing regeneration status resulted in different species replacement patterns within gaps in primary vs. secondary forests. Gap formation in both primary and secondary forests increased species richness, with 33, 38, 39, and 41 in the primary closed stands, primary forest gaps, secondary closed stands, and secondary forest gaps, respectively. However, only 35.9% of species in primary forest gaps and 34.1% in secondary forest gaps successfully reached the upper layer. Based on the importance values (IVs) of tree species across different canopy heights, light-demanding trees in the upper layer of the secondary forests were gradually replaced by intermediate and shade-tolerant trees. In the primary forests, Korean pine exhibited intermittent growth patterns at different canopy heights, while it had continuous regeneration along vertical height gradients in the secondary forests. The differences in Korean pine regeneration between the primary and secondary forests existed before gap formation and continued during the gap-filling processes. The interspecific competition among different tree species gradually decreased with increasing vertical height, and compared to the primary forests, the secondary forests showed an earlier occurrence of competition exclusion within gaps. Our findings revealed the species replacement patterns within gaps and provided a further understanding of the competition dynamics among tree species during the gap-filling processes.

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height.