Tree Counts Research Articles

An investigation of the monitoring and management of the habitat of the family Ardeidae in Muan Yongwol-ri Breeding Sites in Korea

The purpose of this study was to investigate the variation in populations and species of the family Ardeidae through an evaluation of environmental habitat characteristics in the breeding sites of the family Ardeidae in Muan Yongwol, Korea. The study area has been protected as a Natural Monument (No. 211) in Korea and is located in Yongwol, Muan, in Korea. The monitoring of the herons was performed in October 2015 and from February to September 2016. The monitoring of vegetation, fish, benthic macroinvertebrates, and amphibian was performed in April and July of 2016, and the monitoring of land insecta was conducted in July 2016. The populations of the Ardeidae family in the Muan Yongwol-ri habitat of egrets and herons were recorded as 840, 1155, 1211, and 52 for Ardea cinerea, Egretta alba, Egretta garzetta, and Nycticorax nycticorax, respectively. Bubulcus ibis was not found. Terrestrial insects were found in 10 orders, 62 families, and 142 species. Benthic macroinvertebrates were in 11 orders, 15 families, and 18 species, and the predominant species was Chironomidae sp. Fish were found in two orders, three families, and nine species, and Carassius auratus were dominant. The proportions of fish, amphibian, reptilia, insecta, and others for food of Ardeidae were observed at 56.9%, 20.2%, 6.4%, 14.7%, and 1.8%, respectively. The area of the nest trees of Ardeidae family was approximately 8805 m2. The nest tree counts of Pinus thunbergii, Quercus serrata, and Quercus acutissima in the breeding sites were 147, 3, and 3, respectively, and all dead trees were Pinus thunbergii. The levels of salinity and available phosphate were much higher in the breeding site of the Ardeidae family than they were in control area. A management plan should be followed in this breeding area, such as understory vegetation thinning, soil improvement, or transplanting nest trees.

Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network

Providing an accurate evaluation of palm tree plantation in a large region can bring meaningful impacts in both economic and ecological aspects. However, the enormous spatial scale and the variety of geological features across regions has made it a grand challenge with limited solutions based on manual human monitoring efforts. Although deep learning based algorithms have demonstrated potential in forming an automated approach in recent years, the labelling efforts needed for covering different features in different regions largely constrain its effectiveness in large-scale problems. In this paper, we propose a novel domain adaptive oil palm tree detection method, i.e., a Multi-level Attention Domain Adaptation Network (MADAN) to reap cross-regional oil palm tree counting and detection. MADAN consists of 4 procedures: First, we adopted a batch-instance normalization network (BIN) based feature extractor for improving the generalization ability of the model, integrating batch normalization and instance normalization. Second, we embedded a multi-level attention mechanism (MLA) into our architecture for enhancing the transferability, including a feature level attention and an entropy level attention. Then we designed a minimum entropy regularization (MER) to increase the confidence of the classifier predictions through assigning the entropy level attention value to the entropy penalty. Finally, we employed a sliding window-based prediction and an IOU based post-processing approach to attain the final detection results. We conducted comprehensive ablation experiments using three different satellite images of large-scale oil palm plantation area with six transfer tasks. MADAN improves the detection accuracy by 14.98% in terms of average F1-score compared with the Baseline method (without DA), and performs 3.55–14.49% better than existing domain adaptation methods. Experimental results demonstrate the great potential of our MADAN for large-scale and cross-regional oil palm tree counting and detection, guaranteeing a high detection accuracy as well as saving the manual annotation efforts.

Using tree cores to evaluate historic atmospheric concentrations and trends of polycyclic aromatic compounds in the Oil Sands region of Alberta, Canada

Tree cores and bark were sampled from jack pine trees at 18 sites in the Athabasca Oil Sands Region (AOSR) of Alberta, Canada, to investigate spatial and temporal trends of polycyclic aromatic compounds (PACs). Spatial trends were investigated in the bark samples, where ΣPAC concentrations ranged from 75 to 3615 ng/g. Highest concentrations were observed from trees within 40 km of the nearest mining or upgrading facility perimeter fence, in line with previous deposition studies in the AOSR. The sampled tree cores were separated into segments representing 5 years of growth/atmospheric collection by counting tree rings. A significant increase in PAC concentrations over the lifetime of the tree was observed at sites with the highest PAC concentrations, and the average % increase in concentration from 1970 to 2015 was in line with average % growth in bitumen extraction in the AOSR. Finally, the concentrations in the tree core segments representing collection from 2010 to 2015 were converted into an atmospheric PAC concentration using previously published wood-air partition coefficients. The calculated atmospheric concentrations were within the same range as concentrations reported from the passive atmospheric sampling network in this region. The importance of site location is highlighted, with forest edge sites providing an improved comparison for atmospheric exposure and deposition. This is the first study to use tree cores to calculate an atmospheric concentration of PACs, demonstrating the applicability of this methodology for providing historic atmospheric data.

Ecological restoration for biodiversity conservation triggers response of bark beetle pests and their natural predators

Abstract The restoration of forest structure and function is increasingly being used in boreal forests in order to halt the loss of biodiversity. Often ecological restoration is aimed at increasing the volume of dead and dying trees to enhance the biodiversity of deadwood-dependent organisms, but it may also increase population sizes of pest bark beetle species, even several years following restoration. Herein, we used a large-scale restoration experiment in Northern Sweden to assess the 5 years post-restoration effects of restorative gap cutting and prescribed burning on the populations of a set of economically harmful pest bark beetles (Ips typographus, Polygraphus poligraphus, Tomicus piniperda and Pityogenes chalcographus) and the most important predators of bark beetles, Thanasimus spp. In addition, we assessed the effects of forest stand characteristics at stand and landscape scale on the abundance of I. typographus. Five years post-restoration, gap-cut stands supported the highest abundances of P. poligraphus and contained the highest count of spruce trees newly attacked by bark beetles. By contrast, prescribed burning generally sustained the lowest abundances of pest bark beetles, especially I. typographus and P. poligraphus, and the highest abundance of their natural predators Thanasimus spp. The population abundance of I. typographus was also positively affected by the area of clear cuts within a 500 m radius from the stand. In conclusion, prescribed burning appears to be a safer method for ecological restoration than gap cutting in the long-term. According to our results, a risk of a local bark beetle outbreak still remains 5 years following the initiation of ecological restoration treatments on spruce-dominated mature gap-cut stands.

Suitability of Airborne and Terrestrial Laser Scanning for Mapping Tree Crop Structural Metrics for Improved Orchard Management

Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for individual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops.

Modeling Tree Species Count Data in the Understory and Canopy Layer of Two Mixed Old-Growth Forests in the Dinaric Region

The distribution of tree species has traditionally been analyzed based on tree diameter (DBH) as a continuous variable. However, this approach does not usually provide information on how species are distributed across the area of interest. In this study, an inverse approach was applied to investigate tree distribution patterns in two Dinaric old-growth forest stands composed primarily of European beech, silver fir, and Norway spruce. Specifically, the variance-to-mean relationship of tree counts based on 80 plots (40 in each old-growth stand) were evaluated by using a dispersion index. Understory trees exhibited clumped and random patterns, whereas canopy trees were mostly distributed in a random manner. A regular pattern was only determined for beech and all trees in the canopy layer (two cases out of ten). The observed discrete variables were further compared with three theoretical distributions. It was found that a Poisson, binomial, and negative binomial model best fitted the observed count data, which, based on the dispersion index, exhibited a random, regular, and clumped pattern, respectively. The frequency of plots with low species presence and complete absence of species was also revealed. Consequently, the analysis and modeling of tree counts can be of practical use for species conservation purposes.

Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

Comparative Analysis of Four Maple Species for Syrup Production in South-Central Appalachia

Sugar maple (Acer saccharum L.) is a key cultural and economic resource from eastern Canada to south-central Appalachia. Environmental uncertainties could create problems for this iconic species, in particular affecting the southern extent of its range and thus increasing the need for alternative species in maple syrup production. To mediate uncertain­ties, some producers tap additional species, including box elder (Acer negundo L.), red maple (Acer rubrum L.), and silver maple (Acer saccharinum L.). For viable marketability, sap from alternative species should be comparable to sugar maple in volume and sugar concentration. In the 2016 and 2017 tapping seasons, data were collected on sap volume and sap sugar concentration (SSC) for each of these maple species. Sap parameter performance data revealed box elder and to a lesser extent silver maple as the most appropriate alternative species for syrup production in the south-central Appa­lachian region, while red maple, which is a com­monly tapped species in northern regions, per­formed comparably in SSC but very poorly in sap volume in this study. Diversifying sap sources could provide additional sap and tree counts avail­able to producers, allowing for more varied man­agement strategies to mediate climatic variations and uncertainties. This diversification can also allow for industry expansion into areas without sufficient sugar maples and potentially create a new product niche in the maple industry, which can promote rural economic development in south-central Appalachia through ways compatible with other sustainable agroforestry and outdoor tourism efforts. See the press release for this article.

On the Bi-Lipschitz Geometry of Lamplighter Graphs

In this article we start a systematic study of the bi-Lipschitz geometry of lamplighter graphs. We prove that lamplighter graphs over trees bi-Lipschitzly embed into Hamming cubes with distortion at most~$6$. It follows that lamplighter graphs over countable trees bi-Lipschitzly embed into $\ell_1$. We study the metric behaviour of the operation of taking the lamplighter graph over the vertex-coalescence of two graphs. Based on this analysis, we provide metric characterizations of superreflexivity in terms of lamplighter graphs over star graphs or rose graphs. Finally, we show that the presence of a clique in a graph implies the presence of a Hamming cube in the lamplighter graph over it. An application is a characterization in terms of a sequence of graphs with uniformly bounded degree of the notion of trivial Bourgain-Milman-Wolfson type for arbitrary metric spaces, similar to Ostrovskii's characterization previously obtained in \cite{ostrovskii:11}.

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing.

Automated Identification of Crop Tree Crowns from UAV Multispectral Imagery by Means of Morphological Image Analysis

Within the context of precision agriculture, goods insurance, public subsidies, fire damage assessment, etc., accurate knowledge about the plant population in crops represents valuable information. In this regard, the use of Unmanned Aerial Vehicles (UAVs) has proliferated as an alternative to traditional plant counting methods, which are laborious, time demanding and prone to human error. Hence, a methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed. After image acquisition, the captures are processed by means of photogrammetry to yield a 3D point cloud-based representation of the study plot. To exploit the elevation information contained in it and eventually identify the plants, the cloud is deterministically interpolated, and subsequently transformed into a greyscale image. This image is processed, by using mathematical morphology techniques, in such a way that the absolute height of the trees with respect to their local surroundings is exploited to segment the tree pixel-regions, by global statistical thresholding binarization. This approach makes the segmentation process robust against surfaces with elevation variations of any magnitude, or to possible distracting artefacts with heights lower than expected. Finally, the segmented image is analysed by means of an ad-hoc moment representation-based algorithm to estimate the location of the trees. The methodology was tested in an intensive olive orchard of 17.5 ha, with a population of 3919 trees. Because of the plot’s plant density and tree spacing pattern, typical of intensive plantations, many occurrences of intra-row tree aggregations were observed, increasing the complexity of the scenario under study. Notwithstanding, it was achieved a precision of 99.92%, a sensibility of 99.67% and an F-score of 99.75%, thus correctly identifying and geolocating 3906 plants. The generated 3D point cloud reported root-mean square errors (RMSE) in the X, Y and Z directions of 0.73 m, 0.39 m and 1.20 m, respectively. These results support the viability and robustness of this methodology as a phenotyping solution for the automated plant counting and geolocation in olive orchards.

An enhanced hybrid Pareto metaheuritic algorithm-based multicast tree estimation for reliable multicast routing in VANETs

Prompt and reliable data dissemination among the vehicular nodes of the network is indispensable as its mobility rate and limited coverage characteristics introduce the possibility of frequent topology changes. The effective and efficient sharing of critical information in the event of emergency necessitates either direct interaction or Road Side Units (RSUs)-based vehicular communication in the primitive place. Multicast routing is confirmed to be the significant scheme of data transfer since they establish reliable data dissemination between the source and destination vehicular nodes by estimating an optimal multicast tree. Moreover, QoS-constraint enforced meta-heuristic approaches are considered to be excellent for determining optimal multicast tree under multicasting. An Enhanced Hybrid Pareto Metaheuritic Algorithm-based Multicast Tree Estimation Scheme (EHPMA-MTES) is contributed for reliable multicast routing. The proposed EHPMA-MTES is confirmed to reduce the cost of transmission by 28% through the minimization of the multicast tree count formed during the process of multicast routing.

Tree Census Using Circular Hough Transform and GRVI

Abstract Remote sensing techniques provide a potentially low-cost alternative to field-based assessments like tree census, tree crown and tree type identification. But it requires the development of algorithms for accurate extraction of the required information. The data generated out of Multispectral imagery (MSI) with high-spatial-resolution systems provides an opportunity for analysis and mapping of the tree crowns. Tree crown mapping with MSI is generally done by the use of the standard Normalized Difference Vegetation Index (NDVI). MSI can also be taken from Google Earth. Google Earth images have only the red, green and blue band imagery. The response of vegetation can also be reliably detected by means of a simple threshold, using the Green Red Vegetation Index (GRVI). Our method consists of the combination of range detection for GRVI and Circular Hough Transform (CHT) for tree crown extraction. The crown identification also helps in the tree count. Our experiments reveal that the proposed approach yields state-of-the-art tree counts for different datasets. For the first dataset the completeness and the correctness are found to be 0.81 and 0.95. The completeness and correctness are found to be 0.97 and 0.84 respectively for the second dataset.

An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing

Country olive forests are one of the major contributors with economic aspects. Spain is the leading country in the world which produced around 45% of olive oil compared to the total production in the world. The value of 2.4 million hectares of land is dedicated to olive cultivation in the country, which made it a huge olive oil producer in the world. Olive crop widely spread over large extensive areas; manual counting of trees is humanly infeasible. To address this problem, we propose an automatic scheme for the detection and enumeration of olive trees. The proposed technique comprises of multi-step image processing techniques applied over a single band of imagery. The single red band once extracted from the color spectrum of acquired images, is then sharpened and edges are detected. The closed edges formed by the tree boundaries are transformed into white blobs using morphological reconstruction. Resulting circular blobs are then filtered out based on their shape and size. Blobs with circular geometry and in-range radius are considered as olive trees, which are then mapped with the existing ground information. Results have been generated over the diverse images capturing the ground truth information with an estimation error of 1.27%.

Determinant-Preserving Sparsification of SDDM Matrices

We show that variants of spectral sparsification routines can preserve the total spanning tree counts of graphs. By Kirchhoff's matrix-tree theorem, this is equivalent to preserving the determinant...

Estimating biomass and carbon sequestration of plantations around industrial areas using very high resolution stereo satellite imagery

Abstract: Plantations established in highly-pollutant industrial areas have a crucial role to absorb greenhouse gases, particularly CO2. A thorough monitoring of their aboveground biomass and carbon balance is essential to ensure their beneficial effects. This can be operationally supported by using a combination of field and multispectral stereo remote sensing data to provide surface height information with high resolution and wide coverage. We estimated the fresh and dry aboveground biomass and the carbon sequestration from pairs of Pleiades satellite imagery of 25-year-old monoculture plantations of Pinus eldarica Medw., Cupressus arizonica Greene, Morus alba L. and Robinia pseudoacacia L., around the Mobarakeh Steel Complex near the megacity Isfahan. This complex is the largest-scale of its kind in semi-arid Iran. Tree heights were derived from a Canopy height model (CHM) at plantation management unit level. Parsimonious regression models were developed, and the accuracy was assessed by the coefficient of determination, bias and root mean square errors (RMSEs) at plot level. This resulted in R2 of total biomass, dry biomass, carbon sequestration, tree height and tree count of 0.90, 0.90, 0.91, 0.89, and 0.88, respectively. Moreover, mixed bias (with lowest value of -0.12 m for tree height) and NRMSE% (with lowest value of 5.93 % for tree carbon sequestration) values were obtained. The results demonstrated that pairs of stereo imageries can be effectively used for predicting forest biomass and carbon sequestration across semi-arid plantations, hence enabling a continuous monitoring of vegetation established around pollutant industrial areas.

Species Composition and Carbon Stock of Selected Secondary Growth Forest Patches in Ilog-Hilabangan Watershed Forest Reserve

A tree species composition and carbon stock assessment was conducted in selected secondary growth forest patches at Ilog Hilabangan Watershed Forest Reserve in Barangay Buenavista, Himamaylan City, Negros Occidental. The study aimed to (1) identify and enumerate the various tree species, (2) characterize the community structure of tree species in terms of species composition, diversity, evenness and richness, and (3) estimate the carbon stock of the remaining forest patches. In each sampling site, three 100-m transects were laid out, from which three 20m x 20m plots were established. The data collected from each sampling plots include tree count, species identification, and diameter at breast height. A total of 66 species of trees, belonging to 47 genera and 28 families were identified. The dominant species found were Parashorea malaanonan (pi=0.14) in NIDCO, Ficus variegata (pi=0.20) in Cansirmon and Shorea contorta (pi=0.39) in Candi-is. Forest patches in Cansirmon had the highest species richness (Dmg=12.75), while NIDCO had the highest tree species diversity (H’=3.01) and evenness (D=0.12). The highest carbon stock was estimated from NIDCO forest patches, with an average of 194.56 tons/hectare. This can be attributed to the dominance of old and larger trees in the sampling site. The study concluded that the level of disturbance of the site influences its diversity and carbon stock.

Space Use by Prairie Warblers in Regenerating Mixed-oak Forests of Central Pennsylvania

Setophaga discolor (Prairie Warbler) is a Nearctic–Neotropical migratory songbird that is experiencing long-term population declines. A potentially important driver behind these decreases is the loss of shrubland and early successional forest communities in the eastern United States. Central to conservation for species like the Prairie Warbler is an understanding of breeding habitat requirements at the scale of the home range. Despite the Prairie Warbler being identified as a species of continental conservation concern, few studies have explicitly quantified habitat conditions within its home ranges. We used radio telemetry to quantify Prairie Warbler use of space during the breeding season as well as associated vegetation data across 3 study sites in north-central Pennsylvania from May to June 2016. We radio-tracked 11 adult male Prairie Warblers. Using the telemetry locations, we estimated kernel densities and classified minimum convex polygons around kernel densities to define males' home ranges and core-use areas. The average home range (95% kernel) was 6.4 ha, and the average core-use area (50% kernel) was 0.73 ha. Prairie Warbler core-use areas contained more shrubs and lower tree count than peripheral portions of home ranges. To this end, management activities that promote a dense shrub layer across early successional communities should benefit Prairie Warblers. Results from this study serve as baseline data that can be used to help direct future studies at the home-range scale of Prairie Warbler breeding-season ecology.

Bayesian spatial analysis of hardwood tree counts in forests via MCMC

AbstractIn this paper, we use a Bayesian spatial model to spatially interpolate forest inventory data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian generalized linear geostatistical model and implement a Markov chain Monte Carlo algorithm to sample from its posterior distribution. How spatial predictions for new sites in the forests change as the amount of training data is reduced is studied and compared with a Bayesian logistic regression model without a spatial effect. Finally, we discuss a stratified sampling approach for selecting subsets of data that allows for potential better predictions.

Quantifying the uncertainty of variance partitioning estimates of ecological datasets

An important objective of experimental biology is the quantification of the relationship between predictor and response variables, a statistical analysis often termed variance partitioning (VP). In this paper, a series of simulations is presented, aiming to generate quantitative estimates of the expected statistical uncertainty of VP analyses. We demonstrate scenarios with considerable uncertainty in VP estimates, such that it significantly reduces the statistical reliability of the obtained results. Especially when a predictor variable of a dataset shows a low between-group variance, VP estimates may show a high margin of error. This becomes particularly important when the respective predictor variable only explains a small fraction of the overall variance, or the number of replicates is particularly small. Moreover, it is demonstrated that the expected error of VP estimates of a dataset can be approximated by bootstrap resampling, giving researchers a tool for the quantification of the uncertainty associated with an arbitrary VP analysis. The applicability of this method is demonstrated by a re-analysis of the Oribatid mite dataset introduced by Borcard and Legendre in 1994 and the Barro Colorado Island tree count dataset by Condit and colleagues. We believe that this study may encourage biologists to approach routine statistical analyses such as VP more critically, and report the error associated with them more frequently.