Reference Tree Research Articles

Developing GIS-Based Algorithm of Stand Spatial Structure Index and Its Implementation

Accurate information of the fine-scale spatial patterns of trees and their interactions within a stand is critical for explaining the competition, health and vigor status, and future development of a stand. There are a number of indices which can show such patterns, but the stand spatial structure index is the most important. This index can be quantified based on the spatial information of trees (tree positions) within a stand and has paramount importance in identifying candidate trees to be thinned. This study develops a software tool (algorithm), which can conveniently and accurately estimate the stand spatial structure index. Our proposed algorithm considers the spatial relationships between a reference tree and its four adjacent trees, and computes the three most important stand structure indices (neighborhood comparison, uniform angle index and species mingling) using GIS (ArcEngine) and the C# programming language. The implementation of the developed algorithm (stand spatial structure index) shows that, for any reference tree assumed, its four adjacent trees identified by each of the three stand spatial structure indices (uniform angle index—UAI, mingling—M and neighborhood comparison—NC) are the same, indicating the consistency and accuracy between the three-stand spatial structural indices. For the same tree species in a forest stand, the computational results from each of the spatial structure indices used (UAI, M, NC) are also the same. Thus, the results of this algorithm developed in this study are consistent with that of the Winkelmass1.0 software (a type of software used to simulate stand spatial structure). As this article is based on the GIS technique, the computational results can be visually displayed and implemented on actual maps, making it more convenient and intuitive for forest management. The proposed approach will be useful for accurately identifying the trees to be thinned and helpful for maintaining the vigor stand structure. This study also demonstrates the implementation of the algorithm to the real-world data and proves that the computational process is simple and efficient. The application of this algorithm for the identification of trees to be thinned may help the stakeholders to focus their attention towards multi-functional forest management. The algorithm will also provide an important basis for optimizing thinning and maintaining well-structured forest stands.

A novel framework of smart monitoring to face the challenges of tree management in historic gardens

Historic gardens are green spaces characterised by tree stands with several veteran specimens of high artistic and cultural value. Such valuable plant components have to cope with biotic and abiotic stress factors as well as ongoing senescence processes. Maintaining tree health is therefore crucial to preserve their ecosystem services, but also to protect the monument and visitor health. In this context, finding smart, fast and cost-effective management solutions to monitor health and detect critical conditions for both stands and individual veteran trees can promote garden conservation. For this reason, we developed a novel framework based on Sentinel2 imagery, LiDAR sources and automatic cameras to identify risk spots regarding trees in historic gardens. The pilot study area consists of two closed Italian gardens from the 16th century, which were analysed as a unique Historic Garden System (HGS). The tree health status at stand level was assessed using a criterion based on the Normalized Difference Vegetation Index weighed on tree volume (NDVIt) and validated by a visual crown defoliation assessment. At the tree level, the health status of four veteran trees defined by the NDVIt was also evaluated using green chromatic coordinates (GCC) obtained from digital images acquired by cameras at daily intervals during one growing season. The 33% of the tree population was classified as being in poor health, i.e. “at risk”. Veteran trees classified as “at risk” showed an anticipation of phenological phases and a lower GCC compared to reference trees. Despite variability determined by Sentinel medium resolution, the proposed framework showed good accuracy (0.74) for monitoring historical gardens. The semi-automatic risk point mapping system tested here proved to be effective in facilitating the management of historic gardens, which in turn could be applied in the wider context of urban greening.

An updated LSU database and pipeline for environmental DNA identification of arbuscular mycorrhizal fungi

Recent work established a backbone reference tree and phylogenetic placement pipeline for identification of arbuscular mycorrhizal fungal (AMF) large subunit (LSU) rDNA environmental sequences. Our previously published pipeline allowed any environmental sequence to be identified as putative AMF or within one of the major families. Despite this contribution, difficulties in implementation of the pipeline remain. Here, we present an updated database and pipeline with (1) an expanded backbone tree to include four newly described genera and (2) several changes to improve ease and consistency of implementation. In particular, packages required for the pipeline are now installed as a single folder (conda environment) and the pipeline has been tested across three university computing clusters. This updated backbone tree and pipeline will enable broadened adoption by the community, advancing our understanding of these ubiquitous and ecologically important fungi.

Description of Asgardarchaeum abyssi gen. nov. spec. nov., a novel species within the class Asgardarchaeia and phylum Asgardarchaeota in accordance with the SeqCode

Asgardarchaeota, commonly referred to as Asgard archaea, is a candidatus phylum-rank archaeal clade that includes the closest archaeal relatives of eukaryotes. Despite their prevalence in the scientific literature, the name Asgardarchaeota lacks nomenclatural validation. Here, we describe a novel high-quality metagenome-assembled genome (MAG), AB3033_2TS, proposed to serve as the nomenclatural type for the species Asgardarchaeum abyssiTS according to the rules of the SeqCode. Based on protein content and compositional features, we infer that A. abyssi AB3033_2TS is an acetogenic chemoheterotroph, possibly a facultative lithoautotroph, and is adapted to a thermophilic lifestyle. Utilizing genomes from Asgard archaea, TACK, and Euryarchaea, we perform phylogenomic reconstructions using the GTDB archaeal marker genes, the current reference set for taxonomic classification. Calibrating relative evolutionary divergence (RED) values for Asgardarchaeota using established Thermoproteota lineages in the GTDB r207 reference tree, we establish a robust classification and propose Asgardarchaeum as the type genus for the family Asgardarchaeaceae (fam. nov)., the order Asgardarchaeales (ord. nov.), the class Asgardarchaeia (class. nov.), and the phylum Asgardarchaeota (phyl. nov.). This effort aims to preserve taxonomic congruence in the scientific literature.

Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach.

Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, the training phase of DEPP does not scale to more than roughly 10 000 backbone species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331 270 species. This paper explores divide-and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP. While divide-and-conquer has been extensively used in phylogenetics, applying divide-and-conquer to data-hungry machine-learning methods needs nuance. C-DEPP uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing 20 million 16S fragments on the GG2 reference tree in 41 h of computation. The dataset and C-DEPP software are freely available at https://github.com/yueyujiang/dataset_cdepp/.

Using Hyperspectral Images and Lidar Data to Create Models for the Classification and CAVE Visualization of Tree Species

The article describes the use of artificial intelligence methods to create models for detecting and classifying tree species, using hyperspectral images and LiDAR data in the aerial photography of energy line structures. The most important output is a validated model with cloud infrastructure support for detecting and classifying objects of interest at the TRL 5 level, which is also exceptional on a global scale. The outputs of the research are also a geodatabase of reference tree characteristics, a library of spectral curves, a database of simulation of tree growth, but also a cloud infrastructure to support the development of classification models and data storage. An important output will be the visualization of the results of simulations in the "CAVE" environment.

CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model.

Traditional alignment-based methods meet serious challenges in genome sequence comparison and phylogeny reconstruction due to their high computational complexity. Here, we propose a new alignment-free method to analyze the phylogenetic relationships (classification) among species. In our method, the dynamical language (DL) model and the chaos game representation (CGR) method are used to characterize the frequency information and the context information of k-mers in a sequence, respectively. Then for each DNA sequence or protein sequence in a dataset, our method converts the sequence into a feature vector that represents the sequence information based on CGR weighted by the DL model to infer phylogenetic relationships. We name our method CGRWDL. Its performance was tested on both DNA and protein sequences of 8 datasets of viruses to construct the phylogenetic trees. We compared the Robinson-Foulds (RF) distance between the phylogenetic tree constructed by CGRWDL and the reference tree by other advanced methods for each dataset. The results show that the phylogenetic trees constructed by CGRWDL can accurately classify the viruses, and the RF scores between the trees and the reference trees are smaller than that with other methods.

Performance evaluation of individual tree detection and segmentation algorithms using ALS data in Chir Pine (Pinus roxburghii) forest

The application of individual tree detection algorithms for assessing forest inventories and aiding decision-making in forestry has been a subject of research for more than two decades. Nevertheless, there is a notable research gap in the development of robust algorithms capable of automatically detecting trees of different species, ages, and varied crown sizes in dense forest environments. In this study, we conducted a comprehensive evaluation of six different individual tree detection (ITD) algorithms using airborne LiDAR data in Chir Pine (Pinus roxburghii) forests. This research represents one of the pioneering efforts in applying ITD algorithms to Chir Pine forests using ALS data. We categorized ITD routines into two groups: those reliant on local maxima as treetops and initial seeds for crown delineation, and those that do not require explicit treetop identification. To assess accuracy, we developed a special Individual Tree Matching (ITM) algorithm, enabling the matching of LiDAR-detected trees with 284 reference trees measured in the field. Our analysis involved various combinations of filtering fixed window sizes and adaptive window sizes applied to the point cloud, unsmoothed, and smoothed canopy height model (CHM). Our results highlighted the effectiveness of the 3 × 3m fixed window size method on unsmoothed CHM, achieving an overall F-score of 0.65 and a tree detection rate of 86%. Additionally, the Dalponte 2016 method proved superior for crown segmentation using identified treetops, consistently measuring mean crown radii within 0.5 m of reference field trees. Among the methods not relying on treetops, the adaptive mean shift algorithm (AMS3D) delivered strong performance, boasting an overall F-score of 0.67 and mean crown radii within 0.1 m. Our study revealed a high correlation between LiDAR-detected tree heights and field-measured tree heights across all evaluated methods. Overall, our findings underscore the potential of ITD algorithms in enhancing forest attribute measurement accuracy and facilitating climate-responsive forest management strategies.

Robust expansion of phylogeny for fast-growing genome sequence data.

Massive sequencing of SARS-CoV-2 genomes has urged novel methods that employ existing phylogenies to add new samples efficiently instead of de novo inference. 'TIPars' was developed for such challenge integrating parsimony analysis with pre-computed ancestral sequences. It took about 21 seconds to insert 100 SARS-CoV-2 genomes into a 100k-taxa reference tree using 1.4 gigabytes. Benchmarking on four datasets, TIPars achieved the highest accuracy for phylogenies of moderately similar sequences. For highly similar and divergent scenarios, fully parsimony-based and likelihood-based phylogenetic placement methods performed the best respectively while TIPars was the second best. TIPars accomplished efficient and accurate expansion of phylogenies of both similar and divergent sequences, which would have broad biological applications beyond SARS-CoV-2. TIPars is accessible from https://tipars.hku.hk/ and source codes are available at https://github.com/id-bioinfo/TIPars.

A new method for voxel‐based modelling of three‐dimensional forest scenes with integration of terrestrial and airborne LiDAR data

Abstract Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied. We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared. Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06. We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests.

Rapid PRRSV-2 ORF5-based lineage classification using Nextclade.

Porcine reproductive and respiratory syndrome virus (PRRSV) continues to be a global challenge for swine health. Yim-Im et al. 2023 provides a standard genetic nomenclature, extending previously published works to better characterize PRRSV-2 ORF5-based genetic lineages on a global scale. To facilitate the use of this nomenclature, scaffold sequences, including historical and contemporary vaccines, were synthesized into a dataset designed for Nextclade v3.0. Metadata from the scaffold sequences representing year, country, and RFLP typing of the sequence were incorporated into the dataset. These scaffold sequences were processed through the Augur pipeline using DQ478308.1 as a reference strain for rooting and comparison. The resultant classifier can be accessed through the Nextclade website (https://clades.nextstrain.org/) or a link on the PRRSView homepage (https://prrsv.vdl.iastate.edu/). The resultant classifier functions the same as other classifiers hosted by the Nextclade core group and can provide phylogenetic-based PRRSV-2 ORF5 classifications on demand. Nextclade provides additional sequence metrics such as classification quality and notable mutations relative to the reference. The submitted sequences are grafted to the reference tree using phylogenetic placement, allowing for comparison to nearby sequences of reference viruses and vaccine strains. Additional comparisons between sequences can be made with metadata incorporated in the dataset. Although Nextclade is hosted as a webtool, the sequences are not uploaded to a server, and all analysis stay strictly confidential to the user. This work provides a standardized, trivial workflow facilitated by Nextclade to rapidly assign lineage classifications to PRRSV-2, identify mutations of interest, and compare contemporary strains to relevant vaccines.

Wood Material Properties of Forest Fire-Damaged Norway Spruce and Scots Pine for Mechanical Wood Processing in Finland

Due to climate change, the risk of forest fires has increased in Europe, resulting in challenges in the allocation of salvaged wood. We studied the raw material potential for wood products of Norway spruce and Scots pine sawn log trees that remained standing after a large forest fire in Kalajoki, Finland, in July 2021. Eight burned trees, with four reference trees per species, were sampled as standard specimens, and measurements were analyzed with linear mixed models. The effects of fire on the modulus of elasticity and rupture, Brinell hardness, moisture gradient, and color were measured on clear wood specimens of sapwood and heartwood. The wood density, level of fire damage, and height location of a tree were used as additional predictors. The results show some changes in the sapwood material. Spruce wood underwent stronger changes after the fire than pine wood, probably due to spruce wood having a thinner bark and a longer crown. The moisture content decreased in spruce, and the color darkened in both spruce and pine. Changes in the mechanical properties were mostly negligible, but a small increase in the Brinell hardness in spruce and a small decrease in the modulus of rupture in pine were observed. Fresh salvaged wood can be a suitable material for middle-quality and lower-quality wood products. The spread of char and soot into wood and wood processing machinery still limits its usage, especially for spruce.

VISUAL LIDAR ODOMETRY USING TREE TRUNK DETECTION AND LIDAR LOCALIZATION

Abstract. This paper presents a method of visual LiDAR odometry and forest mapping, leveraging tree trunk detection and LiDAR localization techniques. In environments like dense forests, where smooth GPS signals are unreliable, we employ camera and LiDAR sensors to accurately estimate the robot's position. However, forested or orchard settings introduce unique challenges, including a diverse mixture of trees, tall grass, and uneven terrain. To address these complexities, we propose a distance-based filtering method to extract data composed solely of tree trunk information from 2D LiDAR. By restoring arc data from the LiDAR sensor to its circular shape, we obtain position and radius measurements of reference trees in the LiDAR coordinate system. Then, these values are stored in a comprehensive tree trunk database. Our approach combines visual-based SLAM and LiDAR-based SLAM independently, followed by an integration step using the Extended Kalman Filter (EKF) to improve odometry estimation. Utilizing the obtained odometry information and the EKF, we generate a tree map based on observed trees. In addition, we use the tree position in the map as the landmark to reduce the localization error in the proposed SLAM algorithm. Experimental results show that the loop-closing error ranges between 0.3 to 0.5 meters. In the future, it is expected that this method will also be applicable in the fields of path planning and navigation.

EPIK: precise and scalable evolutionary placement with informative k-mers.

Phylogenetic placement enables phylogenetic analysis of massive collections of newly sequenced DNA, when de novo tree inference is too unreliable or inefficient. Assuming that a high-quality reference tree is available, the idea is to seek the correct placement of the new sequences in that tree. Recently, alignment-free approaches to phylogenetic placement have emerged, both to circumvent the need to align the new sequences and to avoid the calculations that typically follow the alignment step. A promising approach is based on the inference of k-mers that can be potentially related to the reference sequences, also called phylo-k-mers. However, its usage is limited by the time and memory-consuming stage of reference data preprocessing and the large numbers of k-mers to consider. We suggest a filtering method for selecting informative phylo-k-mers based on mutual information, which can significantly improve the efficiency of placement, at the cost of a small loss in placement accuracy. This method is implemented in IPK, a new tool for computing phylo-k-mers that significantly outperforms the software previously available. We also present EPIK, a new software for phylogenetic placement, supporting filtered phylo-k-mer databases. Our experiments on real-world data show that EPIK is the fastest phylogenetic placement tool available, when placing hundreds of thousands and millions of queries while still providing accurate placements. IPK and EPIK are freely available at https://github.com/phylo42/IPK and https://github.com/phylo42/EPIK. Both are implemented in C++ and Python and supported on Linux and MacOS.

Resistance to Bark Beetle Outbreak in Norway Spruce: Population Structure Analysis and Comparative Genomic Assessment of Surviving (LTS) and Randomly Selected Reference Trees

Norway Spruce (Picea abies (L.) H. Karst.), a timber species of significant economic and ecological importance in the Northern Hemisphere, faces increasing threats imposed by drought and bark beetle infestation intensified by ongoing climate change. Despite the extensive mortality within stands, a small proportion of mature trees remarkably survive during severe bark beetle outbreaks. Hypothesizing that bark beetle resilience is genetically determined and thus is under natural selection, we anticipated that there is a genetic variation in genome regions linked to the respective resistance in surviving trees. In the Bohemian Forest, restricted to the area of the Czech–Austrian–German border, we identified those resistant individuals, referred to as the “Last Trees Standing” (LTS). Concurrently, we collected reference samples from randomly selected individuals from natural regeneration within concerned sites (seedlings, young trees) and in adjacent unaffected stands (mature trees). Genomic data were generated on a 50K SNPs genotyping array. We conducted a population genetic study based on the Discriminant Analysis of Principal Components (DAPC) method as well as the Genome-Wide Association Study (GWAS). We identified 12 markers (SNPs) significantly associated with tree survival using this approach. Three of those SNPs are located within the genes with the known function in Arabidopsis thaliana orthologs. After further confirmation, we argue that the identified SNPs can be instrumental in identifying trees of higher resistance to bark beetle infestation.

Selection of the Optimal Timber Harvest Based on Optimizing Stand Spatial Structure of Broadleaf Mixed Forests

There is increasing interest in optimizing stand structure through forest management. The forest structure influences growth and maintains the structure, promoting sustainability. Structure-based forest management (SBFM), which is based on the spatial relationships between a reference tree and its four nearest neighbors, considers the best spatial structure for the stand and promotes the development towards a healthy and stable state by selectively thinning specific trees. This management method is a scientific approach for sustainable forest management, and appropriate harvesting is the core principle of uneven-aged forest management. However, the application of this approach in the management of uneven-aged mixed stands is a challenge because their dynamics are more difficult to elucidate than those of planted or pure stands. This study presented a stand spatial structure optimization model with a transition matrix growth model for selecting suitable timber harvest during uneven-aged mixed-forest management optimization. The model was developed using three neighborhood-based structural indices (species mingling, diametric differentiation, and horizontal spatial pattern) and diameter diversity indices. The approach was applied to four broadleaf stands in the Maoershan Forest Farm of the Heilongjiang Province. The results demonstrate that optimizing the stand spatial structure with a transition matrix growth model improved the objective function values (F-index) by 23.8%, 12.8%, 14.6%, and 28.3%, and the optimal removal of trees from the stands ranged from 24.3% to 25.5%. The stand structure in the next cycle (after 5 years) was closer to the uneven-mixed state. The main conclusion of this study is that optimizing the stand spatial structure with a transition matrix growth model can improve the speed and accuracy of tree selection for harvesting in unevenly mixed forests, thus helping regulate stable and diverse forest growth.

PhyloKey: a novel method to rapidly and reliably identify species in complex, species-rich genera, and an opportunity for ‘non-molecular museomics’

AbstractWe present a novel identification tool called PhyloKey, based on the method of morphology-based, phylogenetic binning developed within the software package RAxML. This method takes a reference data set of species for which both molecular and morphological data are available, computes a molecular reference tree, maps the morphological characters on the tree, and computes weights based on their level of consistency versus homoplasy using maximum likelihood (ML) and maximum parsimony (MP). Additional units for which only morphological data are known are then binned onto the reference tree, calculating bootstrap support values for alternative placements. This approach is modified here to work as an identification tool which uses the same character coding approach as interactive keys. However, rather than identifying individual samples through a progressive filtering process when entering or selecting characters, query samples are binned in batch mode to all possible alternative species in the tree, with each placement receiving a bootstrap support adding to 100% for all alternative placements. In addition to the fact that, after scoring a character matrix, a large number of specimens can be identified at once in short time, all possible alternative identifications are immediately apparent and can be evaluated based on their bootstrap support values. We illustrate this approach using the basidiolichen genus Cora, which was recently shown to contain hundreds of species. We also demonstrate how the PhyloKey approach can aid the restudying of herbarium samples, adding further value to these collections and contributing with large quantitative data matrices to ‘non-molecular museomics’. Our analysis showed that PhyloKey identifies species correctly with as low as 50% of the characters sampled, depending on the nature of the reference tree and the character weighting scheme. Overall, a molecular reference tree worked best, but a randomized reference tree gave more consistent results, whereas a morphological reference tree performed less well. Surprisingly, even character weighting gave the best results, followed by parsimony weighting and then maximum likelihood weighting.

Non-native tree species growth characteristics inventoried three decades after planting in the Danube Lowland

Abstract This paper presents non-native (NN) coniferous and deciduous tree species growth and vitality assessment on experimental research plots established in 1980, 1982 and 1985 in the Danube Lowland over an extended time period. For NN coniferous tree species, Douglas fir, grand fir and eastern white pine reached significantly higher values of diameter at breast height (dbh) and height (H) than Austrian pine. For NN deciduous tree species markedly higher values of H and dbh for black walnut compared to sweet chestnut and red oak were recorded. Damage observed in larger extent on examined NN tree species stands included illegal removal of top of stem for grand fir and presence of necrosis on trunks of sweet chestnut. Comparison of NN coniferous and deciduous tree species growth with native reference tree species showed that NN Douglas fir, grand fir, eastern white pine, red oak and black walnut were capable to achieve similar or even higher values of assessed parameters than native reference tree species. The results suggest, that cultivation of NN coniferous, including Douglas fir, eastern white pine, as well as NN deciduous tree species, including red oak and black walnut in Danube Lowland could contribute to diversification and stabilisation of wood production potential of local forest formations.

Greengenes2 unifies microbial data in a single reference tree

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

Urban Forest Above-Ground Biomass Estimation Based on UAV 3D Real Scene

(1) Background: A three-dimensional (3D) real scene is a digital representation of the multidimensional dynamic real-world structure that enables the realistic and stereoscopic expression of actual scenarios, and is an important technological tool for urban refinement management. The above-ground biomass (AGB) of urban forests is an important indicator that reflects the urban ecological environment; therefore, the accurate estimation of AGB is of great significance for evaluating urban ecological functions. (2) Methods: In this study, multiangle aerial photographs of urban street trees were obtained via an unmanned aerial vehicle (UAV) single-lens five-way flight, from 0°, 0°, 90°, 180°, 270°, and five other directions. The multiple view stereo (MVS) algorithm was used to construct three-dimensional realistic models of two tree species: ginkgo and camphor. Then, structural parameters such as tree height, crown diameter, and crown volume were estimated from the 3D real-scene models. Lastly, single-tree AGB models were developed based on structural parameters. (3) Results: The results of this study indicated the following: (A) The UAV visible-light realistic 3D model had clear texture and truly reflected the structural characteristics of two tree species, ginkgo and camphor. (B) There was a significant correlation between the reference tree height, crown diameter and crown volume obtained from the realistic 3D model and the measured values; the R2 for ginkgo height was 0.90, the R2 for camphor crown diameter was 0.87, and the R2 for ginkgo crown volume was 0.89. (C) The accuracy of the AGB estimation models constructed with tree height and canopy volume as variables was generally higher than that of models with tree height and canopy diameter; the model with the highest accuracy of AGB estimation for ginkgo was the linear model with a validation accuracy R2 of 0.96 and RMSE of 8.21 kg, while the model with the highest accuracy of AGB estimation for camphor was the quadratic polynomial model with a validation accuracy R2 of 0.92 and RMSE of 27.74 kg. (4) Conclusions: This study demonstrated that the UAV 3D real-scene model can achieve high accuracy in estimating single-wood biomass in urban forests. In addition, for both tree species, there was no significant difference between the AGB estimates based on the UAV 3D real scene and LiDAR and the measured AGB. These results of urban single-wood AGB estimation based on the UAV 3D real-scene model were consistent with those of LiDAR and even with the measured AGB. Therefore, based on the UAV 3D real-scene model, the single-wood biomass can be estimated with high accuracy. This represents a new technical approach to urban forest resource monitoring and ecological environment function evaluation.