The endodermis plays a critical role in root function by regulating the movement of water and nutrients. Because endodermal function emerges from coordinated interactions among neighboring cells, the three-dimensional (3D) organization of cellular networks may influence how transport pathways are spatially arranged within root tissues. However, the 3D cellular network topology of the endodermis and its potential functional significance in woody plants remain poorly understood. Here, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta. We found that the balsa endodermis exhibits a distinct network topology characterized by higher local connectivity, lower closeness centrality, and lower edge betweenness centrality than that of Eucalyptus. Confocal Raman spectroscopy revealed broadly similar lignin and suberin signatures in the Casparian strip of the two species. Physiological measurements further showed that balsa roots exhibited significantly higher K + influx than Eucalyptus roots. Together, these observations indicate an association between variation in endodermal network organization and differences in root ion transport characteristics. This study highlights the value of integrating three-dimensional cellular reconstruction with network analysis to investigate structure-function relationships in plant tissues.
Why it matches plant phenotyping methodsLSFMによる3D細胞再構築とネットワーク解析が、根内皮の形態・構造特性を定量化する中心的手法として用いられているため、植物フェノタイピング手法の実質的応用に該当する。
abstractHere, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta.
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Why it matches plant phenotyping methods植物形質を分光情報から推定し、ハイパースペクトル画像へ展開して可燃性関連の植物状態を評価する手法が研究の中心であり、モデル性能も検証しているため。
abstractSpectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.
Why it matches plant phenotyping methods植物の複雑形質を予測するゲノム・フェノミック選抜用Rパッケージを開発し、データ準備からモデル評価までの再利用可能なワークフローを提供・検証しているため、フェノタイピング関連ソフトウェアとして中心的です。
abstractThis study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction.
Reproduction assets foundThe paper's authors publicly released the MTMEGPS R package (analysis code/workflow) on GitHub, and the independent multi-environment maize validation dataset (phenotypes and genotypes) is publicly available via the Genomes to Fields initiative DOI. Both are paper-specific, public, and actionable.Dataset · publicnal phenotypic information.
2.2
Independent multi-environment maize validation dataset
The datasets analyzed in this study were obtained from the Genomes to Fields (G2F) initiative ( www.genomes2fields.org ). The dataset comprises 135 unique maize hybrids evaluated across nine experimental sites during the 2018 growing season ( https://doi.org/10.25739/anqq-sg86 ). Phenotypic measurements were collected following standardized protocols provided by the G2F consortium, as detailed in the accompanying documentation available on the project website.
The traits evaluated in this study included plant height (distance from the plant base to the ligule of the flag leaf), ear height (distance fOpen asset ↗10.25739/anqq-sg86lines:51-61Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Oct 2025Computers and Electronics in Agriculture.
The rising prevalence of Phytophthora diseases in forests highlights the need for rapid, non-invasive detection methods. Early-stage root infections are difficult to detect due to the absence of visible above-ground symptoms, while current diagnostics remain slow and invasive. This study investigated whether hyperspectral leaf reflectance could detect root rot caused by Phytophthora alticola in Eucalyptus benthamii. Nineteen commercially planted families were inoculated, and leaf spectra were collected using an ASD FieldSpec 4 sensor. A machine learning pipeline was developed to identify diagnostic spectral signals. Key wavelengths were identified using permutation importance, a genetic algorithm, and self-attention network (SAN) scores. Spectral signals linked to root rot revealed that infection was correlated with leaf pigment accumulation and moisture stress. Three algorithms, random forest (RF), support vector machine (SVM), and SAN, were trained on hyperspectral data to predict P. alticola infection. The SAN achieved 97 % accuracy on a reduced dataset, which included the diagnostic wavelengths from the feature selection step, surpassing the RF (96 %) and SVM (94 %) models. This study demonstrates hyperspectral sensing as an effective tool for detecting Phytophthora root rot using spectra from the foliage and highlights the application of advanced machine learning techniques for plant disease classification.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から根腐病という植物の病態を推定するセンシングと機械学習パイプラインが研究の中心であり、特徴選択と分類性能も評価しているため。
abstractA machine learning pipeline was developed to identify diagnostic spectral signals.
Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment.
Why it matches plant phenotyping methodsRGB-NIR衛星画像から樹冠高を推定するモデルを評価し、UAV由来の高さと比較検証している。植物の形態形質である樹冠高の取得手法が研究の中心である。
Water deficit stress (WDS) can negatively affect the development, productivity and quality of Eucalyptus spp. To minimize this impact, the development of high throughput techniques for early and accurate WDS detection is necessary. This study focuses in the use of visible-near infrared hyperspectral imaging (VIS-NIR HSI) and chemometric methods to detect water deficit spectral patterns of these species and to develop predictive models for early detection of water deficit level in juvenile plants. The research included the analysis of four Eucalyptus genotypes, two Eucalyptus globulus and two Eucalyptus gloni (hybrid of E. nitens and E. globulus). Forty ramets of each genotype were submitted to WDS conditions associated with different stress levels and compared with control samples using conventional physical characterization and common vegetative indices of plants, besides VIS-NIR HSI. The HSI data were analyzed using principal component analysis (PCA) and supervised pattern recognition methods to classify the samples by WDS level using as validation set the mean spectra of images (bulk prediction) and all the pixels of whole plant (single pixel prediction). The results of PCA showed a differentiated response on the different WDS conditions, especially at day 10 of water deficit. Conventional vegetation indices, such as NDVI, PRI and MCARI, did not detect indications of an early WDS response, while pattern recognition methods including partial least squares (PLS-DA), discriminant support vector machine (SVM-DA) and k-nearest neighbor (KNN) showed a remarkable predictive ability for WDS level with a prediction error (Err) of 2 % in external validation sets. Supervised models were applied also to reconstruct the stress level in all the pixels of whole plants. The superior effectiveness of the SVM-DA and KNN models to predict stress level in images of Eucalyptus spp. provided valuable information on spatial distribution of stress in the plant.
Why it matches plant phenotyping methodsVIS-NIRハイパースペクトル画像とケモメトリクスを用いて、植物の水分欠乏ストレスを早期検出・分類する予測手法を開発し、外部検証と画素単位のストレス分布推定を行っており、表現型取得・抽出法が中心である。
abstractthe development of high throughput techniques for early and accurate WDS detection is necessary
Automatic collection of tree-level crown information is essential for sustainable forest management and fine carbon stock estimation. UAV-based light detection and ranging (LiDAR) and UAV-based multi-angle photogrammetry (UMP) data depict the 3D structure of forests at a fine-grained level by generating detailed point clouds, making them potential alternatives to labor-intensive forest inventories. However, the accuracy of the individual tree crown segmentation algorithms that have been developed is unstable in forest stands with high terrain undulation and high canopy density, mainly due to the various crown sizes and interlocking crowns resulting in varying degrees of over- or under-segmentation. Here, we propose self-similarity cluster grouping (SCG) algorithm for individual tree crown segmentation that integrates multivariable calculus of crown surfaces and spectral-texture-color spatial information of crown. Firstly, according to the property that DSM and its multi-order gradient information can characterize the crown surface variation and concavity-convexity features, first- and second-order edge detection operators were used to preliminarily determine the crown patch edges in order to reduce under-segmentation. Then, we developed a self-similarity weight function controlled by the spectral, texture and color spatial information of the tree crown patches to increase the similarity difference between adjacent crown patches of the same tree and those of neighboring trees, and designed the strategy for cluster grouping crown patches to complete individual tree crown segmentation. The performance of the proposed SCG algorithm was verified in Mytilaria, Red oatchestnu, Chinese fir and Eucalyptus plots in subtropical forests of China using LiDAR and UMP data. The overall accuracy of F-score (f) was above 0.85 for crown segmentation, and the rRMSE for crown width, crown area and crown circumference extractions reached 0.13, 0.22 and 0.14, respectively. On this basis, we evaluated the effect of spatial resolution of DSM on the segmentation accuracy of SCG algorithm, and found that the crown segmentation accuracy was proportional to the spatial resolution. Compared to the normalized cut algorithm, marker-controlled watershed algorithm and threshold-based cloud point segmentation algorithm, the SCG algorithm improved the overall accuracy f of individual tree crown segmentation by 0.06, 0.13 and 0.05 for LiDAR and 0.06, 0.21 and 0.10 for UMP, respectively. Furthermore, the effectiveness and generalizability of the SCG algorithm was verified in other Mytilaria, Red oatchestnut, Chinese fir and Eucalyptus plots in subtropical forests and Larch and Chinese pine plots in temperate forests using UMP data. The crown segmentation accuracy was better than 0.82, and the crown width extraction accuracy was up to 89 %. Overall, our proposed SCG algorithm reduces the over- and under-segmentation in complex forest structures and provides technical support for accurate crown information extraction at both plot and forest stand levels.
Why it matches plant phenotyping methodsUAV LiDAR・写真測量から個体樹冠を分割し、樹冠幅・面積・周長を抽出するSCG手法の開発、比較検証、汎化評価が論文の中心であるため。
abstractHere, we propose self-similarity cluster grouping (SCG) algorithm for individual tree crown segmentation that integrates multivariable calculus of crown surfaces and spectral-texture-color spatial information of crown.
The portable X-ray fluorescence (pXRF) spectrometry has been very useful for the characterization of different earth materials, and its application for foliar analysis is really promising. The performance of pXRF for foliar analysis depends on several factors such as concentration of the elements, fluorescence yield which is influenced by atomic number, spectral interference, and water content. Mn is one of the elements that present a prominent fluorescence peak. In this sense, it was hypothesized that pXRF can directly determine the Mn concentration on foliar samples, even when used on intact leaves (fresh or dry) being a useful tool for agronomic and environmental purposes. Thus, the objective was to assess the performance of a pXRF to determine Mn concentration in two different foliar datasets from Brazil/South America and Mali/Africa. In the Brazilian dataset, leaves from eight crops (common bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) were scanned via pXRF at the following conditions: intact and fresh leaves, intact and dry leaves, and powdered samples). In the Malian dataset, powdered samples from cotton and maize were analyzed via pXRF. For comparison, Mn concentration was also determined after nitro-perchloric digestion followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES). After descriptive statistics, linear regressions were performed for all sample preparation conditions in both datasets, using Mn concentrations obtained through pXRF and the acid digestion method. The data quality level of all linear regressions was considered quantitative with high R (0.93 to 0.98) and R 2 (0.87 to 0.96) values. The direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%). The manufactured pXRF calibration used in this work allowed an accurate direct Mn determination in plant leaves. Considering the importance of Mn as a plant micronutrient and its potential toxicity depending on soil redox conditions, the fast, in situ, non-destructive, and eco-friendly determination via pXRF has a tremendous agronomic and environmental application worldwide.
Why it matches plant phenotyping methods植物葉のMn濃度という生理・元素形質を、携帯型XRFで非破壊測定する方法の性能評価と検証が中心であり、単なる生物学的実験での routine 測定ではない。
abstractThe direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%).
In an era where global climate change is shifting plant phenology, global meta-analyses of multiple species are required more than ever. Common language or references for enhanced data compatibility are key for such analyses. Although the Plant Phenology Ontology (PPO) addresses this challenge, it does not capture several relevant reproductive structures that are critical in species with long reproductive cycles, like many Eucalyptus species. We reviewed the terminology and concepts that describe reproductive structures in eucalypts and compared them with the existing classes of the PPO to explore the PPO's potential for harmonizing disparate eucalypt datasets. We identified incongruencies within and between eucalypt terminology and the PPO. We tested the sensitivity of the PPO for capturing key eucalypt phenological structures and found it sensitive to classification of certain structures. To address these limitations, we developed the Eucalyptus Phenology Ontology (EPO), a new ontology that builds on the PPO and captures key reproductive structures using a more refined classification. The EPO integrates the relationships between reproductive structures, phenological stages, and phenological traits. The vocabulary is species-neutral so it can be applied to other taxa but specifies the synonyms and descriptions required to capture the complexity of eucalypt phenology.
Why it matches plant phenotyping methodsユーカリの生殖構造・フェノロジー形質を標準化して記録する新規オントロジーを開発しており、植物フェノタイピングデータの取得・統合基盤が中心である。
abstractTo address these limitations, we developed the Eucalyptus Phenology Ontology (EPO), a new ontology that builds on the PPO and captures key reproductive structures using a more refined classification.
Genomic and phenomic prediction (GP and PP, respectively) are innovative methods that allow plant breeders to increase the productivity of crops. Traditional methods for conducting GP and PP typically rely on linear regression models with predefined assumptions and cannot capture the complex relationships between genotypes and phenotypes. Deep learning models are focused on nonlinear algorithms that can potentially capture complex nonadditive effects, including dominance and epistasis, presenting an opportunity for improving GP and PP accuracy. In this study, the predictions of deep learning models (convolutional neural networks: CNN and multilayer perceptron: MLP) were compared those of Bayesian alphabet models (BayesA, BayesB, BayesCπ, Bayesian lasso, and Bayesian ridge regression) using both single nucleotide polymorphisms (for GP) and spectral information (for PP) datasets for eucalyptus trees adapted to arid environments. The deep learning models consistently outperformed the Bayesian models in predicting most traits, with accuracy estimates ranging from 0.13 to 0.80 for the MLP, 0.16–0.82 for the CNN, and 0.08–0.66 for the Bayesian models. Additionally, spectral information significantly enhanced the accuracy in predicting 50 % of the traits, mainly when applied to deep learning models, demonstrating the potential of high-throughput phenotyping techniques combined with deep learning models in the prediction of important agronomic traits. Thus, deep learning models and spectral data should be incorporated as a strategy for tree breeding programs.
Why it matches plant phenotyping methodsユーカリのスペクトル情報を用いたフェノミック予測について、深層学習モデルとベイズモデルを比較・検証しており、植物形質の推定手法が研究の中心である。
abstractthe predictions of deep learning models (convolutional neural networks: CNN and multilayer perceptron: MLP) were compared those of Bayesian alphabet models
EucalyptusLaboratory / benchtopRaman / spectroscopyLeafPhysiological trait estimationWater status / transpiration
Drought is one of the main factors contributing to tree mortality worldwide and drought events are set to become more frequent and intense in the face of a changing climate. Quantifying water stress of forests is crucial in predicting and understanding their vulnerability to drought-induced mortality. Here, we explore the use of high-resolution spectroscopy in predicting water stress indicators of two native Australian tree species, Callitris rhomboidea and Eucalyptus viminalis. Specific spectral features and indices derived from leaf-level spectroscopy were assessed as potential proxies to predict leaf water potential (Ψ leaf ), equivalent water thickness (EWT) and fuel moisture content (FMC) in a dedicated laboratory experiment. New spectral indices were identified that enabled very high confidence linear prediction of Ψ leaf for both species (R 2 > 0.85) with predictive capacity increasing when accounting for a breakpoint in the relationships using segmented regression (E. viminalis, R 2 > 0.89; C. rhomboidea, R 2 > 0.87). EWT and FMC were also linearly predicted to a high accuracy (E. viminalis, R 2 > 0.90; C. rhomboidea, R 2 > 0.80). This study highlights the potential of spectroscopy as a tool for predicting measures of plant water noninvasively, enabling broader applications for monitoring and managing plant water stress.
Why it matches plant phenotyping methods葉レベル分光法を用いて植物の水ポテンシャル、含水量、燃料水分を非侵襲的に推定する手法を開発・評価しており、表現型取得が研究の中心です。
abstractwe explore the use of high-resolution spectroscopy in predicting water stress indicators
Genomic and phenomic prediction (GP and PP, respectively) are innovative methods that allow plant breeders to increase the productivity of crops. Traditional methods for conducting GP and PP typically rely on linear regression models with predefined assumptions and cannot capture the complex relationships between genotypes and phenotypes. Deep learning models are focused on nonlinear algorithms that can potentially capture complex nonadditive effects, including dominance and epistasis, presenting an opportunity for improving GP and PP accuracy. In this study, the predictions of deep learning models (convolutional neural networks: CNN and multilayer perceptron: MLP) were compared those of Bayesian alphabet models (BayesA, BayesB, BayesCπ, Bayesian lasso, and Bayesian ridge regression) using both single nucleotide polymorphisms (for GP) and spectral information (for PP) datasets for eucalyptus trees adapted to arid environments. The deep learning models consistently outperformed the Bayesian models in predicting most traits, with accuracy estimates ranging from 0.13 to 0.80 for the MLP, 0.16–0.82 for the CNN, and 0.08–0.66 for the Bayesian models. Additionally, spectral information significantly enhanced the accuracy in predicting 50 % of the traits, mainly when applied to deep learning models, demonstrating the potential of high-throughput phenotyping techniques combined with deep learning models in the prediction of important agronomic traits. Thus, deep learning models and spectral data should be incorporated as a strategy for tree breeding programs.
Why it matches plant phenotyping methodsユーカリのスペクトル情報を用いた表現型予測を対象に、深層学習モデルとベイズモデルを比較・検証しており、植物形質の取得・推定ワークフローが中心的である。
abstractthe predictions of deep learning models (convolutional neural networks: CNN and multilayer perceptron: MLP) were compared those of Bayesian alphabet models
In the forest industry, interspecific hybridization, such as Eucalyptus urograndis (Eucalyptus grandis × Eucalyptus urophylla) and Corymbia maculata × Corymbia torelliana, has led to the development of high-performing F1 generations. The successful breeding of these hybrids relies on verifying progenitor origins and confirming post-crossing, but conventional genotype identification methods are resource-intensive and result in seed destruction. As an alternative, multispectral imaging analysis has emerged as an efficient and non-destructive tool for seed phenotyping. This approach has demonstrated success in various crop seeds. However, identifying seed species in the context of forest seeds presents unique challenges due to their natural phenotypic variability and the striking resemblance between different species. This study evaluates the efficacy of spectral imaging analysis in distinguishing hybrid seeds of E. urograndis and C. maculata × C. torelliana from their progenitors. Four experiments were conducted: one for Corymbia spp. seeds, one for each Eucalyptus spp. batch separately, and one for pooled batches. Multispectral images were acquired at 19 wavelengths within the spectral range of 365-970 nm. Classification models based on Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) was created using reflectance and reflectance features, combined with color, shape, and texture features, as well as nCDA transformed features. The LDA algorithm, combining all features, provided the highest accuracy, reaching 98.15% for Corymbia spp., and 92.75%, 85.38, and 86.00 for Eucalyptus batch one, two, and pooled batches, respectively. The study demonstrated the effectiveness of multispectral imaging in distinguishing hybrid seeds of Eucalyptus and Corymbia species. The seeds' spectral signature played a key role in this differentiation. This technology holds great potential for non-invasively classifying forest seeds in breeding programs.
Why it matches plant phenotyping methodsマルチスペクトル画像と分類モデルを用いて、森林樹種・雑種種子を非破壊で識別するフェノタイピング手法を評価・適用しており、方法が研究の中心である。
abstractAs an alternative, multispectral imaging analysis has emerged as an efficient and non-destructive tool for seed phenotyping.
Quantitative wood anatomy is a subfield in dendrochronology that requires effective open-source image analysis tools. In this research, the bioimage analysis software QuPath (v0.4.4) is introduced as a candidate for accurately quantifying the cellular properties of the xylem in an automated manner. Additionally, the potential of QuPath to detect the transition of early- to latewood tracheids over the growing season was evaluated to assess a potential application in dendroecological studies. Various algorithms in QuPath were optimized to quantify different xylem cell types in Eucalyptus grandis and the transition of early- to latewood tracheids in Pinus radiata. These algorithms were coded into cell detection scripts for automatic quantification of stem microsections and compared to a manually curated method to assess the accuracy of the cell detections. The automatic cell detection approach, using QuPath, has been validated to be reproducible with an acceptable error when assessing fibers, vessels, early- and latewood tracheids. However, further optimization for parenchyma is still required. This proposed method developed in QuPath provides a scalable and accurate approach for quantifying anatomical features in stem microsections. With minor amendments to the detection and classification algorithms, this strategy is likely to be viable in other plant species.
Why it matches plant phenotyping methods植物木部細胞の解剖学的形質を画像から自動抽出するQuPathベースの手法を開発・最適化し、手動法との比較で精度と再現性を検証しているため、フェノタイピング手法が中心です。
abstractthe bioimage analysis software QuPath (v0.4.4) is introduced as a candidate for accurately quantifying the cellular properties of the xylem in an automated manner.
The forest plots survey's primary measurement factors are the diameter at breast height (DBH), tree height, and the individual tree volume. The reconstruction of a 3D forest scene based on dense images can contribute to the automatic extraction of individual tree parameters. Close-range photogrammetry (CRP) can achieve the reconstruction of a 3D scene from the sequence images at a highly efficient and low cost. However, the reconstructed point clouds often miss the trees' upper part, resulting in the tree height and volume not being measured directly. This paper proposes an integrating extraction framework and methods of the DBH, tree height, and individual tree volume for achieving automatic measurement of the individual tree parameters with high accuracy based on CRP. Four plantation plots in Wangyedian Forest Farm, Inner Mongolia, and Gaofeng Forest Farm, Guangxi Zhuang, are selected to obtain sequence close-up photographs and construct 3D scenes. The DBH, tree height, and trunk volume of the individual tree are extracted using circle fitting, stem curve fitting, growth equation, and segmentation measurement. The extracted individual tree parameters from Terrestrial laser scanning (TLS) are used as validation data to evaluate the accuracy. It shows a good performance in two Larix gmelinii plots, one Pinus tabulaeformis plot, and one Eucalyptus grandis plot with the accuracy of extracted DBH above 90%, which is 90.18%, 96.54%, 90.17%, and 95.00%, respectively. The accuracy of extracted tree height of the four plots is all above 85%, which is 93.34%, 91.71%, 90.32%, and 85.96%, respectively, and the accuracy of trunk volume is 91.55%, 91.94%, 89.86%, and 89.6%, respectively. The results show that our proposed integrated parameter extraction frame and methods can achieve high accuracy economically, efficiently, and conveniently, and be used for automatic measurements of individual tree factors in forest plot surveys.
Why it matches plant phenotyping methods近距離写真測量による3D再構成から、個体樹木のDBH・樹高・幹体積を自動抽出する手法を開発し、TLSで精度検証しており、植物形質取得法が研究の中心です。
abstractThis paper proposes an integrating extraction framework and methods of the DBH, tree height, and individual tree volume for achieving automatic measurement of the individual tree parameters with high accuracy based on CRP.
Defoliation by leaf-cutting ants alters the physiological processes of plants, and this defoliation can be inferred from satellite imagery used to identify plant injuries. The aim of this study was to evaluate the spectral pattern of defoliation by leaf-cutting ants in eucalyptus plants on a pixel level using unsupervised machine learning techniques applied to remote sensing by satellites. The study was carried out in a eucalyptus plantation in the municipality of Telêmaco Borba, Paraná state, Brazil. The nests of leaf-cutting ants were located and georeferenced. Multispectral images were obtained from the Sentinel-2 (S-2) and planet scope (PS) satellites. The response variables were the RGB-NIR bands and four vegetation indices (VIs). The data obtained from these bands and vegetation indices was separated in an unsupervised method by the k-medoids clustering algorithm and input into a Random Forest (RF) model. The significance of the models was tested with permutational multivariate analysis of variance (PERMANOVA). The k-medoids algorithm classified the spectral response of the RGB-NIR and VIs bands into two main factors of variation in the tree canopy. The models selected were 1200 trees and 6 variables for the S2 satellite (accuracy = 97.74 ± 0.040%) and 900 trees and 5 variables for the PS (accuracy = 97.42 ± 0.026%). The unsupervised machine learning technique, applied to remote sensing, was effective to map defoliation caused by leaf-cutting ants, and this approach can be used in precision agriculture for pest management purposes.
Why it matches plant phenotyping methods衛星リモートセンシングと教師なし機械学習により、ユーカリ樹冠のアリによる植物状態（脱葉）を推定・分類する手法が研究の中心であり、精度評価も実施している。
abstractThe aim of this study was to evaluate the spectral pattern of defoliation by leaf-cutting ants in eucalyptus plants on a pixel level using unsupervised machine learning techniques applied to remote sensing by satellites.
Abstract This protocol outlines a simple procedure for the measurement and calculation of reflectance and transmittance radiance of eucalyptus leaves in the field using a handheld spectrometer. This protocol is useful as it allows for rapid measurement of leaf optical properties which provide insights into a range of physical and biochemical traits. The procedure requires specialised equipment to be completed, but the procedure can be followed regardless of the brand of the instrument.
Why it matches plant phenotyping methodsユーカリ葉の反射・透過スペクトルを測定する実用的なフィールドプロトコルであり、葉の光学特性や関連する植物形質の取得方法が中心である。
abstractThis protocol outlines a simple procedure for the measurement and calculation of reflectance and transmittance radiance of eucalyptus leaves in the field using a handheld spectrometer.
EucalyptusLeafPhysiological trait estimationBiomass / plant weightLeaf traitsWater status / transpiration
Abstract This protocol outlines the collection procedure of eucalypt leaves for analysis of various plant traits and the measurement of important water and physical traits. Fuel moisture content, dry matter content, equivalent water thickness, and specific leaf area provide insights into the flammability and biomass available for burning and the impact of moisture content on the fuel particles. Calculation of these parameters can all be achieved using the simple gravimetric methods. The key stages of this protocol include the collection of samples (1 day), the calculation of surface area and fresh weight (approximately 20 samples processed in two hours) and the measurement of dry weight (approximately 40 samples processed per hour).
Why it matches plant phenotyping methodsユーカリ葉の水分・物理形質を重力法で測定する手順そのものを提示しており、植物形質取得プロトコルが中心である。
abstractThis protocol outlines the collection procedure of eucalypt leaves for analysis of various plant traits and the measurement of important water and physical traits.
Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation.
Why it matches plant phenotyping methodsTLS点群から樹木構造を再構成し、地上部炭素蓄積量や幹・枝体積を推定する手法を開発し、伐倒調査で精度検証しているため、植物表現型の取得・推定が中心です。
abstractthis paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus
Stomata are the gatekeepers of plant water use and must quickly respond to changes in plant water status to ensure plant survival under fluctuating environmental conditions. The mechanism for their closure is highly sensitive to disturbances in leaf water status, which makes isolating their response to declining water content difficult to characterise and to compare responses among species. Using a small-scale non-destructive nuclear magnetic resonance spectrometer as a leaf water content sensor, we measure the stomatal response to rapid induction of water deficit in the leaves of nine species of eucalypt from contrasting climates. We found a strong linear correlation between relative water content at 50% stomatal conductance (RWC gs50 ) and mean annual temperature at the climate of origin of each species. We also show evidence for stomata to maintain control over water loss well below turgor loss point in species adapted to warmer climates and secondary increases in stomatal conductance despite declining water content. We propose that RWC gs50 is a promising trait to guide future investigations comparing stomatal responses to water deficit. It may provide a useful phenotyping trait to delineate tolerance and adaption to hot temperatures and high leaf-to-air vapour pressure deficits.
Why it matches plant phenotyping methods非侵襲NMRを葉の水分状態センサーとして用い、乾燥応答を定量する新しい表現型RWC gs50を提案しており、測定手法と再利用可能な形質が研究の中心です。
abstractUsing a small-scale non-destructive nuclear magnetic resonance spectrometer as a leaf water content sensor, we measure the stomatal response to rapid induction of water deficit
Selecting individuals in breeding programs for pulp production, the physical and chemical wood traits must be considered in the genotype selection. In this study, trees from hybrid progeny tests of Corymbia torelliana x Corymbia citriodora (CTOxCCT) and progenies of Eucalyptus dunnii (EDU) were investigated. Sawdust of genetically selected standing trees was collected, classified and prepared for near infrared (NIR) spectra readings. The chemical properties of the selected trees were determined through kraft pulping. Predictive models for each property were developed based on the reference data and NIR spectra. Two approaches for models were developed. The first approach, models were fitted with 25 samples of CTOxCCT, and in the second approach, models were fitted using 61 samples (25 of CTOxCCT and 36 of EDU). The estimated R²cv values were 0.60 and 0.73 for basic chip density, 0.37 and 0.65 for extractive contents, 0.56 and 0.53 for total lignin contents, 0.63 and 0.66 for S/G ratio, and 0.52 and 0.77 for screened pulp yield for the first and second approaches, respectively. All developed models have potential for ranking trees in breeding programs. NIR spectroscopy can potentially be applied as a high-throughput field phenotyping tool where thousands of varieties need to be evaluated.
Why it matches plant phenotyping methodsNIRスペクトルから木材の物理・化学形質を推定する予測モデルを開発・評価し、育種向けハイスループット表現型計測ツールとして位置づけているため、植物フェノタイピング手法が中心である。
abstractPredictive models for each property were developed based on the reference data and NIR spectra.
Automatic tree crown mapping, crown width (CW) and crown projection area (CPA) extraction are the basis for high-precision estimation of forest productivity and carbon stock. Terrestrial close-range observation (TCRO) technology, mainly including terrestrial laser scanning (TLS) and close-range photogrammetry (CRP), measures individual tree structural parameters by generating detailed three-dimensional (3D) points, making it a potential replacement for labor-intensive manual forest inventories. However, accurate extraction of crown parameters using TCRO in high-density forests remain a challenge due to different degrees of missing crown information caused by occlusion between trees and limitations of computer 3D modeling techniques and segmentation methods. Here, we propose a model for estimating crown parameters using tree spacing, diameter at breast height (DBH) and stand age, which collaboratively considers the advantages of TCRO data and the bioecological mechanisms of inter-tree competition. First, an approach to construct a tree triangulation network with tree position and tree spacing is designed based on the analysis of the bioecological mechanisms of inter-tree competition relationships. Second, a model to quantify inter-tree competition using tree spacing, DBH and stand age is developed to extract CW and CPA. We demonstrate the generality of our model to high-density forests in subtropical China with TLS data. It shows a good performance in Chinese fir, Eucalyptus and Spingbract Chinkapin-dominated plots with estimation accuracy (EA) ≥ 90.22 % and relative root mean square error (rRMSE) ≤ 0.1286 for CW and EA ≥ 84.51 % and rRMSE ≤ 0.1861 for CPA. In addition, the performance in estimating CW (EA = 89.80 %) and CPA (EA = 86.13 %) using CRP point clouds demonstrate the applicability of our model to CRP. The proposed model collaboratively considers inter-tree competition mechanism and TCRO data characteristics, therefore it is interpretable in terms of bioecological principle and universal for different forest environments (e.g., coniferous, broad-leaved and mixed coniferous species). The results show that the method provides an efficient solution for automatic and accurate measurements of the crown parameters in the forest sample plot investigation, which can support the fine forest management and carbon stock estimation.
Why it matches plant phenotyping methodsTLS・近距離写真測量データから樹冠幅と樹冠投影面積を自動抽出するモデルを開発・検証しており、個体の形態形質取得が研究の中心である。
abstractAutomatic tree crown mapping, crown width (CW) and crown projection area (CPA) extraction are the basis for high-precision estimation of forest productivity and carbon stock.
Supervised learning-based methods can identify crop threats in the visual data collected by an Unmanned Aerial Vehicle (UAV). However, as these methods induce classification models from a finite set of a priori known classes, they cannot recognize new patterns emerging in visual data to be classified. In agricultural environments, these patterns may appear over time, so that those related to diseases/pests should be addressed by the classifier timely. This study investigates an extension of a semi-supervised classification algorithm to identify new classes of threats appearing in UAV visual data. To do so, the algorithm aggregates information from clusters with Support Vector Machine (SVM) outcomes operating on the unlabeled (target) data. From an iterative active learning procedure, the classification model is then fed back to learn a new class. Experimental results showed that our algorithm can discover a new threat, named Ceratocystis wilt, in Eucalyptus plantations even with labeled data scarcity and class imbalance. Also, even this new class being the minority one, its error rate was reduced to almost zero in few iterations on a tested dataset. This is due to the adopted Entropy and Density-based Selection approach, which explored the new class better than an SVM Margin Sampling baseline. When operating on VGGNet-16 deep features, our algorithm achieved accuracies between 92% and 97% being slightly better than those results based on hand-crafted features.
Why it matches plant phenotyping methodsUAV画像からユーカリの病害状態を直接推定する新規クラス検出・分類手法が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis study investigates an extension of a semi-supervised classification algorithm to identify new classes of threats appearing in UAV visual data.
Shoot and leaf blight caused by Teratosphaeria destructans is one of the most devastating foliar diseases on Eucalyptus . Therefore, breeding for resistance to this disease is considered urgent. Differences in susceptibility to T. destructans have been observed in the field but a robust inoculation protocol has, until recently, been unavailable and a disease scoring method for precise phenotyping has not been established. A first objective of this study was to determine the optimal conidial concentration for T. destructans inoculations on a susceptible Eucalyptus host. This concentration was then used to determine differences in susceptibility of six genotypes of Eucalyptus grandis × E. urophylla to the pathogen by assessing the percentage of infected stomata using electron microscopy and the percentage of leaf area covered by lesions (PLACL) using image processing. In addition, we developed a disease susceptibility index (SI) of six categories ranging from highly resistant (SI = 0) to highly susceptible (SI = 1.5 to 2). The more resistant genotypes were moderately resistant, with an SI value of 0.49 to 0.54 and a PLACL of 6.5 to 9%. In contrast, the more susceptible genotype scored an SI of 1.52 and PLACL of 48%. Host susceptibility was also assessed relative to the sporulation of the pathogen. This showed that the percentage of sporulation was not significantly correlated with host resistance. Overall, the results provide the basis for rigorous screening and selection of resistant genotypes to the disease caused by T. destructans using artificial inoculation.
Why it matches plant phenotyping methodsユーカリの病害抵抗性を定量するため、画像処理による病斑面積測定と疾病感受性指数を開発し、人工接種によるスクリーニングへの適用基盤を示しており、表現型取得法が研究の中心である。
abstracta disease scoring method for precise phenotyping has not been established
Light detection and ranging (LiDAR) has been a tool of choice for 3D dense point cloud reconstructions of forest canopy over the past two decades, but advances in computer vision techniques, such as structure from motion (SfM) photogrammetry, have transformed 2D digital aerial imagery into a powerful, inexpensive and highly available alternative. Canopy modelling is complex and affected by a wide range of inputs. While studies have found dense point cloud reconstructions to be accurate, there is no standard approach to comparing outputs or assessing accuracy. Modelling is particularly challenging in native eucalypt forests, where the canopy displays abrupt vertical changes and highly varied relief. This study first investigated whether a remotely sensed LiDAR dense point cloud reconstruction of a native eucalypt forest completely reproduced canopy cover and accurately predicted tree heights. A further comparison was made with a photogrammetric reconstruction based solely on near-infrared (NIR) imagery to gain some insight into the contribution of the NIR spectral band to the 3D SfM reconstruction of native dry eucalypt open forest. The reconstructions did not produce comparable canopy height models and neither reconstruction completely reproduced canopy cover nor accurately predicted tree heights. Nonetheless, the LiDAR product was more representative of the eucalypt canopy than SfM-NIR. The SfM-NIR results were strongly affected by an absence of data in many locations, which was related to low canopy penetration by the passive optical sensor and sub-optimal feature matching in the photogrammetric pre-processing pipeline. To further investigate the contribution of NIR, future studies could combine NIR imagery captured at multiple solar elevations. A variety of photogrammetric pre-processing settings should continue to be explored in an effort to optimise image feature matching.
Why it matches plant phenotyping methodsLiDARとNIRフォトグラメトリによる森林キャノピー再構成を比較し、樹高とキャノピー被覆の推定精度を評価することが中心であり、植物形質取得法の技術検証に該当する。
abstractThis study first investigated whether a remotely sensed LiDAR dense point cloud reconstruction of a native eucalypt forest completely reproduced canopy cover and accurately predicted tree heights.
EucalyptusRaman / spectroscopyStem / branchPhysiological trait estimationWater status / transpiration
Tree breeding programs and wood industries require simple, time- and cost-effective techniques to process large volumes of samples. In recent decades, near-infrared spectroscopy (NIRS) has been acknowledged as one of the most powerful techniques for wood analysis, making it the most used tool for high-throughput phenotyping. Previous studies have shown that a significant number of anatomical, physical, chemical and mechanical wood properties can be estimated through NIRS, both for angiosperm and gymnosperm species. However, the ability of this technique to predict functional traits related to drought resistance has been poorly explored, especially in angiosperm species. This is particularly relevant since determining xylem hydraulic properties by conventional techniques is complex and time-consuming, clearly limiting its use in studies and applications that demand large amounts of samples. In this study, we measured several wood anatomical and hydraulic traits and collected NIR spectra in branches of two Eucalyptus L'Hér species. We developed NIRS calibration models and discussed their ability to accurately predict the studied traits. The models generated allowed us to adequately calibrate the reference traits, with high R2 (≥0.75) for traits such as P12, P88, the slope of the vulnerability curves to xylem embolism or the fiber wall fraction, and with lower R2 (0.39-0.52) for P50, maximum hydraulic conductivity or frequency of ray parenchyma. We found that certain wavenumbers improve models' calibration, with those in the range of 4000-5500 cm-1 predicting the highest number of both anatomical and functional traits. We concluded that the use of NIRS allows calibrating models with potential predictive value not only for wood structural and chemical variables but also for anatomical and functional traits related to drought resistance in wood types with complex structure as eucalypts. These results are promising in light of the required knowledge about species and genotypes adaptability to global climatic change.
Why it matches plant phenotyping methodsユーカリ木材の解剖学的・水理学的植物形質をNIRSで推定する校正モデルを開発・評価しており、表現型取得手法が研究の中心である。
abstractIn this study, we measured several wood anatomical and hydraulic traits and collected NIR spectra in branches of two Eucalyptus L'Hér species.
The quantitative, accurate and efficient acquisition of tree phenotypes is the basis for forest “gene-phenotype-environment” studies. It also offers significant support for clarifying the genetic control mechanisms of tree traits. The application of unmanned aerial vehicle (UAV) remote sensing technology to the collection of phenotypic traits at an individual tree level quantitatively analyses tree phenology and directionally evaluates tree growth, as well as accelerating the process of forest genetics and breeding. In this study, with the help of high-resolution, high-overlap, multispectral images obtained by an UAV, combined with digital elevation models (DEMs) extracted from point clouds acquired by a backpack LiDAR, a high-throughput tree structure and spectral phenotypic traits extraction and a genetic selection were conducted in a trial of Eucalyptus clones in the State-owned Dongmen Forest Farm in the Guangxi Zhuang Autonomous Region. Firstly, we validated the accuracy of extracting the phenotypic parameters of individual tree growth based on aerial stereo photogrammetry point clouds. Secondly, on this basis, the repeatability of the tree growth traits and vegetation indices (VIs), the genetic correlation coefficients between the traits were calculated. Finally, the eucalypt clones were ranked by integrating a selection index of traits, and the superior genotypes were selected and their genetic gain predicted. The results showed a high accuracy of the tree height (H) extracted from the digital aerial photogrammetry (DAP) point cloud based on UAV images (R2 = 0.91, and RMSE = 0.56 m), and the accuracy of estimating the diameter at breast height (DBH) was R2 = 0.71, and RMSE = 0.75 cm. All the extracted traits were significantly different within the tree species and among the clones. Except for the crown width (CW), the clonal repeatability (Rc) of the traits were all above 0.9, and the individual repeatability values (Ri) were all above 0.5. The genetic correlation coefficient between the tree growth traits and VIs fluctuated from 0.3 to 0.5, while the best clones were EA14-15, EA14-09, EC184, and EC183 when the selection proportion was 10%. The purpose of this study was to construct a technical framework for phenotypic traits extraction and genetic analysis of trees based on unmanned aerial stereo photography point clouds and high-resolution multispectral images, while also exploring the application potential of this approach in the selective breeding of eucalypt clones.
Why it matches plant phenotyping methodsUAV写真測量点群とマルチスペクトル画像から樹高・DBH・樹冠幅・植生指数などの個体樹木形質を抽出し、精度検証と遺伝的評価を行う方法論が研究の中心である。
abstracta high-throughput tree structure and spectral phenotypic traits extraction
As a faster, reliable, and low cost technique, applicable to large samplings, near infrared (NIR) spectroscopy technology has been widely applied for high-throughput phenotyping in forest breeding programmes. The aim of this study was to develop multivariate models for estimating the chemical and physical properties of juvenile wood based on NIR signatures of milled wood. Moreover, two approaches, namely, external validation by clone and by age, were tested to validate the model for estimating extractive content. NIR spectra of wood specimens taken from three clones of Eucalyptus urophylla (one to six years old) grown in southern Brazil were used to calibrate and validate models for predicting the wood basic density, total extractives, ash content, holocellulose content, syringyl to guaiacyl ratio (S/G) and elementary components of the wood. PLS-R models were validated by an independent set of wood specimens and presented promising statistics for the estimating wood density (R2p = 0.768), extractives (R2p = 0.912), ash (R2p = 0.936) and carbon (R2p = 0.697) contents from NIR signatures measured in the milled wood of young trees. Furthermore, NIR models for estimating the extractive content of wood were validated using the clones or ages left out of the training sets. Most models presented satisfactory statistics (R2 > 90%) and could be applied to routine laboratory analyses or to select potential trees in Eucalyptus breeding programmes.
Why it matches plant phenotyping methodsNIRスペクトルから若齢ユーカリ木材の物理・化学形質を推定するモデルを開発し、クローン別・樹齢別に独立検証しており、森林育種向けの表現型取得手法が中心である。
abstracttwo approaches, namely, external validation by clone and by age, were tested to validate the model for estimating extractive content
Near Infrared Spectroscopy (NIR) is often used to perform high throughput phenotyping on thousands of genotypes using prediction models with high variability. A study was therefore undertaken to analyze the potential of multispecies, multisite and multi-age NIR calibration models of seven chemical properties of eucalyptus wood. The models are based on 358 samples selected among more than 5000 samples that belong to five eucalypt species including hybrids. The samples were collected from trees aged 2-35 originating from four different countries. Spectra were measured on non-extracted wood powders using an FT-NIR spectrometer. Models were established in the spectral range of 9090-4040 cm−1 using the PLS regression method, tested by repeated cross-validation and validated on independent test sets. The results showed that the robust models for total extractives (R2P = 0.91, RMSEP = 1.20%, RPD = 3.3) and KL (R2P = 0.89, RMSEP = 1.21%, RPD = 3.0) provided good predictions. These two properties were the best predicted, followed by the S/G ratio (R2P = 0.84, RMSEP = 0.19, RPD = 2.5) and ASL content (R2P = 0.81, RMSEP of 0.54, RPD = 2.3). For holocellulose, alphacellulose, and hemicelluloses contents, the models provided approximate predictions. The prediction errors were always less than twice of the laboratory errors except for ASL and S/G ratio. For total extractives and ASL, β-coefficients of models were of approximately the same magnitude throughout the 9000-4000 cm−1 region while for the five other properties, they were higher in the 7500-4000 cm−1 region. Models were also established in narrower NIR regions, and the quality of models obtained was about the same as that of the models based in the 9090-4000 cm−1 wide range. These established robust models can be used to make predictions based on samples of high variability.
Why it matches plant phenotyping methodsFT-NIRとPLS回帰による木材化学形質の推定モデルを構築し、反復交差検証と独立テストセットで技術的に検証しているため、植物フェノタイピング手法が中心である。
abstractA study was therefore undertaken to analyze the potential of multispecies, multisite and multi-age NIR calibration models of seven chemical properties of eucalyptus wood.
Individual tree attributes, such as stem volume and biomass, are usually predicted by using traditional field-derived allometric models. However, these models are derived from data collected from small areas and lack a level of detail of tree components (e.g., stem, branches, and leaves). Remote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates. We used a high-density LiDAR data on board of a Unmanned Aerial Vehicle (UAV) to evaluate the performance of the QSM approach in estimating field-derived individual tree attributes such as the diameter at breast height (dbh), tree height (ht), and volume (v), as well as the stem (SAGB), branch (BAGB), and total (TAGB) aboveground biomass of eucalyptus trees. QSM was used in two different approaches: (i) using dbh and h derived from QSM and then applied into the field-based equations for estimation of volume and (ii) deriving tree volume directly from QSM. In general, all fitted models using the QSM approach were satisfactory, but with a slight tendency of over-estimation of dbh (9.33%), ht (12.40%), v-QSM1 (26.35%), v-QSM2 (26.66%), TAGB (27.08%), SAGB (25.57%), and BAGB (20.08%). Non-significant differences were noticed when estimating the dbh, tree volume, stem, and aboveground biomass. Despite the overestimation, this study indicates that using the QSM approach to estimate individual tree attributes from UAV-LiDAR is a promising alternative to support the decision-making process regarding forest management activities, especially when considering tree architecture and biomass components.
Why it matches plant phenotyping methodsUAV-LiDARとQSMにより樹木のdbh、樹高、体積、バイオマスを推定し、実測値との性能評価を行っており、植物形質の取得・推定手法が中心である。
abstractRemote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( hg2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( hg2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( hg2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.
Why it matches plant phenotyping methods葉面スペクトル反射を用いた高スループット表現型計測と、反射データによる化学形質予測・遺伝的評価が研究の中心であり、単なる routine 測定ではない。
abstractan integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed
Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.
Why it matches plant phenotyping methodsUAV-LiDARによる個体樹木検出・森林インベントリ手法を開発し、現地測定と比較して樹木数、胸高断面積、材積を検証しているため、植物形質取得法が中心である。
abstractwe perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD).
Phenotype describes the physical, physiological and biochemical characteristics of organisms that are determined or influenced by genes and environment. Accurate extraction of phenotypic data is a prerequisite for comprehensive forest phenotyping in order to improve the growth and development of forest plantations. Combined with the assessments of genetic characteristics, forest phenotyping will help to accelerate the breeding process, improve stress resistance and enhance the quality of the planted forest. In this study, we disposed our study in Eucalyptus trials within the Gaofeng forest farm (a typical Eucalyptus plantation site in southern China) for a high-throughput phenotypic traits extraction and genetic characteristics analysis based on high-density point clouds (acquired by a UAV-borne LiDAR sensor) and high-resolution RGB images (acquired by a UAV-borne camera), aiming at developing a high-resolution and high-throughput UAV-based phenotyping approach for tree breeding. First, we compared the effect of CHM-based Marker-Controlled Watershed Segmentation (MWS) and Point Cloud-based Cluster Segmentation (PCS) for extracting individual trees; Then, the phenotypic traits (i.e., tree height, diameter at breast height, crown width), the structural metrics (n = 19) and spectral indices (n = 9) of individual trees were extracted and assessed; Finally, a genetic characteristics analysis was carried out based on the above results, and we compared the differences between high-throughput phenotyping by UAV-based data and on manual measurements. Results showed that: in the relatively low stem density site of the trial (760 n/ha), the overall accuracy of MWS and PCS was similar, while in the higher stem density sites (982 n/ha, 1239 n/ha), the overall accuracy of MWS (F(2) = 0.93, F(3) = 0.86) was higher than PCS (F(2) = 0.84, F(3) = 0.74); With the increase of stem density, the difference between the overall accuracy of MWS and PCS gradually expanded. Both UAV–LiDAR extracted phenotypic traits and manual measurements were significantly different across the Eucalyptus clones (P < 0.05), as were most of the structural metrics (47/57) and spectral indices (26/27), revealing the genetic divergence between the clones. The rank of clones demonstrated that the pure clones (of E. urophylla), the hybrid clones (of E. urophylla as the female parent) and the hybrid clones (of E. wetarensis and E. grandis) have a higher fineness of growth. This study proved that UAV-based fine-resolution remote sensing could be an efficient, accurate and precise technology in phenotyping (used in genetic analysis) for tree breeding.
Why it matches plant phenotyping methodsUAV-LiDARとRGB画像を用いた樹木形質の抽出手法を開発・比較し、手動測定と検証した、樹木育種向けの中心的なフェノタイピング研究です。
abstractaiming at developing a high-resolution and high-throughput UAV-based phenotyping approach for tree breeding
The plantation of fast-growing tree species plays a crucial role in supplying forest products to growing economies while relieving pressure on native forests reducing the overexploitation of native ecosystems. Grown in more than 90 countries, Eucalyptus spp. are fast-growing tree species driving economic production comprising pulp, wood, or thermal energy. Climate change is increasing the frequency and severity of extreme drought episodes: the 2019 drought in Mato Grosso, Brazil, was the second driest episode ever recorded. Forest inventory data comprising thousands of individual tree measurements collected in consecutive surveys - years 2019, 2020, 2021 – were modelled with mixed-effects models to identify significant factors influencing tree mortality after drought. Time-series of growth dynamics of the four eucalypt commercial clones were tested. For the landscape assessment, surveys using unmanned aerial vehicle (UAV) supported a high-resolution image mosaicking to derive digital surface models and the vegetation indexes driving segmentation and classification methods using ground plot-level observations as training data. The impact of drought was measured in terms of mortality, growth dynamics and the financial losses computed when valuing the 1400-ha plantation along the 3-year series. The modelling results showed high-stocking plantations suffered more from extreme water-stress conditions. The interaction between stocking and clone was analysed. On average, the 2019 drought triggered an estimate loss of $3500 per hectare. The estimation of live and dead conditions using UAV-based outcomes reached an overall accuracy of 89% and 0.7 in the Cohen’s kappa used to map forest health wall-to-wall. Our findings advocate for a reduction of tree planting density and enhanced clone selection to turn eucalypt plantations more resilient to extreme water-stress. Combining UAV-based remote sensing technology, ground truth data and management expertise in eucalypt management eases the operationalization of data-driven solutions towards more resilient silvicultural guidelines and increased efficiency of forest plantations.
Why it matches plant phenotyping methodsUAV画像から樹木の生死・森林健全性を推定する画像解析ワークフローを構築し、地上観測で学習・検証して精度評価しているため、植物状態の取得手法が実質的に中心である。
abstractsurveys using unmanned aerial vehicle (UAV) supported a high-resolution image mosaicking to derive digital surface models and the vegetation indexes driving segmentation and classification methods using ground plot-level observations as training data.
Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider ( Petauroides volans ), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale from leaf-scale chemistry measurements across a gradient of mixed-species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50-1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross-validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、樹冠レベル・林分スケールの葉面窒素を推定・検証する手法が研究の中心であり、植物形質の取得方法に該当する。
abstractWe tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale
Reproduction assets foundThe paper's data availability statement deposits all datasets and analysis scripts on Dryad (public DOI), while UAV imagery, point clouds and raster data are only available upon request. The Victorian Biodiversity Atlas is a third-party public database of animal observations, not a paper-specific phenotyping asset.Dataset · publicAll other datasets and scripts are available on Dryad via https://doi.org/10.5061/dryad.k0p2ngf9d .Open asset ↗Dryad · 10.5061/dryad.k0p2ngf9dlines:600-681Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Digital aerial photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAV) have been increasingly used for forest inventory and monitoring. In this study, we evaluated the potential of UAV photogrammetry data to detect individual trees, estimate their heights (ht), and monitor the initial silvicultural quality of a 1.5-year-old Eucalyptus sp. stand in northeastern Brazil. DAP estimates were compared with accurate tree locations obtained with real time kinematic (RTK) positioning and direct height measurements obtained in the field. In addition, we assessed the quality of a DAP-UAV digital terrain model (DTM) derived using an alternative ground classification approach and investigated its performance in the retrieval of individual tree attributes. The DTM built for the stand presented an RMSE of 0.099 m relative to the RTK measurements, showing no bias. The normalized 3D point cloud enabled the identification of over 95% of the stand trees and the estimation of their heights with an RMSE of 0.36 m (11%). However, ht was systematically underestimated, with a bias of 0.22 m (6.7%). A linear regression model, was fitted to estimate tree height from a maximum height metric derived from the point cloud reduced the RMSE by 20%. An assessment of uniformity indices calculated from both field and DAP heights showed no statistical difference. The results suggest that products derived from DAP-UAV may be used to generate accurate DTMs in young Eucalyptus sp. stands, detect individual trees, estimate ht, and determine stand uniformity with the same level of accuracy obtained in traditional forest inventories.
Why it matches plant phenotyping methodsUAV写真測量による個体樹木の検出・樹高推定・均一性評価を、RTK位置情報および実測樹高と比較して精度検証しており、植物形質取得手法が研究の中心である。
abstractwe evaluated the potential of UAV photogrammetry data to detect individual trees, estimate their heights (ht), and monitor the initial silvicultural quality
Abstract This study presents a method for three-dimensional (3D) reconstruction of forest tree species that are, for instance, required for simulations of 3D canopies in radiative transfer modelling. We selected three forest species of different architecture: Norway spruce (Picea abies) and European beech (Fagus sylvatica), representatives of European production forests, and white peppermint (Eucalyptus pulchella), a common forest species of Tasmania. Each species has a specific crown structure and foliage distribution. Our algorithm for 3D model construction of a single tree is based on terrestrial laser scanning (TLS) and ancillary field measurements of leaf angle distribution, percentage of current-year and older leaves, and other parameters that could not be derived from TLS data. The algorithm comprises four main steps: (i) segmentation of a TLS tree point cloud separating wooden parts from foliage, (ii) reconstruction of wooden parts (trunks and branches) from TLS data, (iii) biologically genuine distribution of foliage within the tree crown and (iv) separation of foliage into two age categories (for spruce trees only). The reconstructed 3D models of the tree species were used to build virtual forest scenes in the Discrete Anisotropic Radiative Transfer model and to simulate canopy optical signals, specifically: angularly anisotropic top-of-canopy reflectance (for retrieval of leaf biochemical compounds from nadir canopy reflectance signatures captured in airborne imaging spectroscopy data) and solar-induced chlorophyll fluorescence signal (for experimentally unfeasible sensitivity analyses).
Why it matches plant phenotyping methodsTLS点群から樹木の幹・枝・葉の構造を再構築する3D計算法が研究の中心であり、樹木形態・樹冠構造という植物形質の取得に直接関わる。
abstractThis study presents a method for three-dimensional (3D) reconstruction of forest tree species
Abstract In forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンサーとニューラルネットワークにより、ユーカリのDBHと樹高という植物形質を推定する手法が研究の中心である。
abstractHere we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices.
An efficient and accurate forest sample plot survey is of great significance to understand the current status of forest resources at the stand or regional scale and the basis of scientific forest management. Close-range photogrammetry (CRP) technology can easily and quickly collect sequence images with high overlapping to reconstruct the 3D model of forest scenes and extract the individual tree parameters automatically and, therefore, can greatly improve the efficiency of forest investigation and has great application potential in forestry visualization management. However, it has some issues in practical forestry applications. First, the imaging quality is affected by the illumination in the forest, resulting in difficulty in feature matching and low accuracy of parameter extraction. Second, the efficiency of 3D forest model reconstruction is limited under complex understory vegetation or the topographic situation in the forest. In addition, the density of point clouds by dense matching directly affects the accuracy of individual tree parameter extraction. This research collected the sequence images of sample plots of four tree species by smartphones in Gaofeng Forest Farm in Guangxi and Wangyedian Forest Farm in Mongolia to analyze the effects of image enhancement, feature detection and dense point cloud algorithms on the efficiency of 3D forest reconstruction and accuracy of individual tree parameter extraction, then proposed a strategy of 3D reconstruction and parameter extraction suitable for different forest scenes. First, we compared the image enhancement effects of median–Gaussian (MG) filtering, single-scale retinex (SSR) and multi-scale retinex (MSR) filtering algorithms. Then, an improved algorithm combining Harris corner detection with speeded-up robust features (SURF) feature detection (Harris+SURF) is proposed, and the feature matching effect is compared with that of a scale invariant feature transform (SIFT) operator. Third, according to the morphological characteristics of the trees in the sequence images, we used the iterative interpolation algorithm of a planar triangulation network based on geometric constraints (GC-based IIPTN) to increase the density of point clouds and reconstruct the 3D forest model, and then extract the position and DBH of the individual trees. The results show that MSR image enhancement can significantly increase the number of matched point pairs. The improved Harris+SURF method can reduce the reconstruction time of the 3D forest model, and the GC-based IIPTN algorithm can improve the accuracy of individual tree parameter extraction. The extracted position of the individual tree is the same as the measured position with the bias within 0.2 m. The accuracy of extracted DBH of Eucalyptus grandis, Taxus chinensis, Larix gmelinii and Pinus tabuliformis is 94%, 95%, 96% and 90%, respectively, which proves that the proposed 3D model reconstruction method based on image enhancement has great potential for tree position and DBH extraction, and also provides effective support for forest resource investigation and visualization management in the future.
Why it matches plant phenotyping methods森林個体の位置・DBHという明示的な植物形質を、画像強調・特徴照合・3D再構成・点群処理で抽出する方法を開発・比較・検証しており、フェノタイピング手法が研究の中心である。
abstractanalyze the effects of image enhancement, feature detection and dense point cloud algorithms on the efficiency of 3D forest reconstruction and accuracy of individual tree parameter extraction, then proposed a strategy of 3D reconstruction and parameter extraction suitable for different forest scenes.
The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions. This UAS was deployed to assess under-canopy UAS photogrammetry as an alternative to field measurements for obtaining stem diameters as well as ultra-high-resolution (~400,000 points/m2) 3D models of forest study sites. There were 378 tape-based diameter measurements collected from 99 stems in a native, unmanaged eucalyptus pulchella forest with mixed understory conditions and steep terrain. These measurements were used as a baseline to evaluate the accuracy of diameter measurements from under-canopy UAS-based photogrammetric point clouds. The diameter measurement accuracy was evaluated without the influence of a digital terrain model using an innovative tape-based method. A practical and detailed methodology is presented for the creation of these point clouds. Lastly, a metric called the Circumferential Completeness Index (CCI) was defined to address the absence of a clearly defined measure of point coverage when measuring stem diameters from forest point clouds. The measurement of the mean CCI is suggested for use in future studies to enable a consistent comparison of the coverage of forest point clouds using different sensors, point densities, trajectories, and methodologies. It was found that root-mean-squared-errors of diameter measurements were 0.011 m in Site 1 and 0.021 m in the more challenging Site 2. The point clouds in this study had a mean validated CCI of 0.78 for Site 1 and 0.7 for Site 2, with a mean unvalidated CCI of 0.86 for Site 1 and 0.89 for Site 2. The results in this study demonstrate that under-canopy UAS photogrammetry shows promise in becoming a practical alternative to traditional field measurements, however, these results are currently reliant upon the operator’s knowledge of photogrammetry and his/her ability to fly manually in object-rich environments. Future work should pursue solutions to autonomous operation, more complete point clouds, and a method for providing scale to point clouds when global navigation satellite systems are unavailable.
Why it matches plant phenotyping methods森林樹木の幹径という明示的な植物形態形質を、林冠下UAS写真測量で取得する方法を開発・検証しており、点群作成手順と被覆指標も提示しているため、植物フェノタイピング手法が中心である。
abstractThis research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions.
The characterization of 3D vegetation structures is an important topic, which has been addressed by recent research in remote sensing. The forest inventory requires the proper extraction of accurate structural and functional features of individual trees. This paper presents a novel methodology to study the impact of the canopy reflectance on the 3D tree structure. A heterogeneous natural environment in a Mediterranean forest, in which various tree species (pine, oak and eucalyptus) coexist, was covered using a high-resolution digital camera and a multispectral sensor. These devices were mounted on an Unmanned Aerial Vehicle (UAV) in order to observe the tree architecture and the spectral reflectance at the same time. The Structure from Motion (SfM) method was applied to model the 3D structures using RGB images from the high-resolution camera. The geometric accuracy of the resulting point cloud was validated by georeferencing the study area through multiple ground control points (GCPs). Then, the point cloud was enriched with the reflected light in four narrow-bands (green, near-infrared, red and red-edge). Furthermore, the Normalized Difference Vegetation Index (NDVI) was calculated in order to measure the tree vigor. A comprehensive analysis based on structural and spectral features of individual trees was proposed. A spatial segmentation was developed to detect single-trees in a forest and for each one to identify the crown and trunk. Consequently, structural parameters were extracted, such as the tree height, the diameter at breast height (DBH) and the crown volume. The validation of these measurements was performed by field data, which were taken using a Total Station (TS). In addition, these characteristics were correlated with the mean reflectance in the tree canopy. Regarding the observed tree species, a statistical analysis was carried out to study the impact of reflectance on the 3D tree structure. By applying our method, a more detailed knowledge of forest dynamics can be gained and the impact of available solar irradiance on single-trees can be analyzed.
Why it matches plant phenotyping methodsUAV搭載カメラ・マルチスペクトルセンサー、SfM、単木分割を統合し、樹高・DBH・樹冠体積などの植物形質を抽出して現地測定で検証する手法開発が中心である。
abstractThis paper presents a novel methodology to study the impact of the canopy reflectance on the 3D tree structure.
Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.
Why it matches plant phenotyping methodsUAS画像とフォトグラメトリによる樹冠形態の再構成を中心に、スペクトル帯・季節・太陽高度が点群の完全性と再現性に与える影響を検証しているため、植物表現型計測法として適格。
abstractWe examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy.
Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.
Why it matches plant phenotyping methodsUAV-LiDARによる個体木のDBHと樹高という植物形態形質の自動取得・現地測定との技術検証が研究の中心である。
abstractHere, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach
Airborne Laser Scanning (ALS) generates accurate data for calculating forest metrics, such as canopy height, yet can be cost-prohibitive. Satellite-based stereo pair photogrammetry has the potential to overcome this limitation of ALS to facilitate multi-temporal change analysis when ALS data capture is unfeasible; however, it remains largely untested across Australian conservation and production eucalypt forests. This study examined root-mean-square differences (RMSD) between canopy height measurements derived from ALS, field measurements and satellite-based photogrammetry for a spotted gum (Corymbia citriodora) plantation and scribbly gum (Eucalyptus racemosa) woodland in south-east Queensland, Australia. The comparison found satellite-based photogrammetry under predicted canopy height compared to field measurements and ALS, whilst the RMSD indicated low performance for satellite-based photogrammetry across the eucalypt plantation and woodland. The open and heterogenous forest structure typical in eucalypt forests combined with low point cloud density for photogrammetry to inadequately sample the canopy and increase stereo matching errors; which was exacerbated across the open and heterogenous scribbly gum woodland. Current satellite-based photogrammetry is therefore unlikely to provide a viable alternative to ALS when analysing canopy height across eucalypt forests at high-resolution. General surface analysis across large areas of eucalypt forest at moderate resolution, or airborne photogrammetric methods, could demonstrate increased viability as an alternative to ALS.
Why it matches plant phenotyping methods衛星ステレオ写真測量による森林キャノピー高推定をALSおよび現地測定と比較・検証しており、植物形質取得手法の性能評価が研究の中心である。
abstractThis study examined root-mean-square differences (RMSD) between canopy height measurements derived from ALS, field measurements and satellite-based photogrammetry
Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.
Why it matches plant phenotyping methodsUAV画像とALS由来の3D点群、樹冠 delineation、回帰モデルを用いて個体樹木の体積を推定・比較し、精度検証しているため、植物形質取得手法が中心である。
abstractThe main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations.
EucalyptusTissuePhysiological trait estimationWater status / transpiration
Xylem vulnerability to embolism represents an important trait to determine species distribution patterns and drought resistance. However, estimating embolism resistance frequently requires time-consuming and ambiguous hydraulic lab measurements. Based on a recently developed pneumatic method, we present and test the "Pneumatron", a device that generates high time-resolution and fully automated vulnerability curves. Embolism resistance is estimated by applying a partial vacuum to extract air from an excised xylem sample, while monitoring the pressure change over time. Although the amount of gas extracted is strongly correlated with the percentage loss of xylem conductivity, validation of the Pneumatron was performed by comparison with the optical method for Eucalyptus camaldulensis leaves. The Pneumatron improved the precision of the pneumatic method considerably, facilitating the detection of small differences in the (percentage of air discharged [PAD] 2 = 0.93) for E. camaldulensis. By providing an open source platform, the Pneumatron represents an easy, low-cost, and powerful tool for field measurements, which can significantly improve our understanding of plant-water relations and the mechanisms behind embolism.
Why it matches plant phenotyping methods木部キャビテーション脆弱性という植物生理形質を高時間分解能で測定する自動装置を開発し、光学法との比較で検証しているため、方法が研究の中心である。
abstractwe present and test the "Pneumatron", a device that generates high time-resolution and fully automated vulnerability curves.
This paper aims to evaluate and compare the mixed effects modeling and artificial neural networks in order to estimate the taper of eucalyptus in integrated Crop-Livestock-Forestry (iCLF) systems. The data were collected in an experimental area of iCLF, implanted by the Brazilian Company of Farming Research – EMBRAPA Agrossilvipastoril, located in the municipality of Sinop, Mato Grosso State, Brazil. To reach the proposed aim, 165 trees with 51 months of age were scaled for the taper modeling with mixed effects models and artificial neural networks. The performance of these techniques was evaluated through precision measurements and graphical analysis. Mixed effects modeling and artificial neural networks are efficient and recommended in the estimative of taper of eucalyptus in integrated Crop-Livestock-Forestry system; however, despite both evaluated techniques present accurate results in predicting the taper of the sampled trees, the artificial neural network predicts values with greater precision than the modeling of mixed effects.
Why it matches plant phenotyping methodsユーカリ幹のテーパーという植物形態形質を推定するため、混合効果モデルと人工ニューラルネットワークを比較・評価しており、推定手法が研究の中心である。
abstractThis paper aims to evaluate and compare the mixed effects modeling and artificial neural networks in order to estimate the taper of eucalyptus
Plants actively regulate excess absorbed energy to protect photosynthetic machinery through heat dissipation in a process known as non-photochemical quenching (NPQ), a process useful for quantifying plant health and productivity. NPQ can be indirectly measured in the visible wavelengths between 500 nm and 560 nm, most commonly through the Photochemical Reflectance Index (PRI). However, there remains a lack of consensus regarding the optimal functional form and band selection to calculate PRI for the purpose of measuring NPQ mechanisms. Here, we quantitatively evaluate the effectiveness of leaf-level parametric and non-parametric spectral formulations, band locations, and number of bands to track the xanthophyll pigment cycle in a tall mature Eucalypt forest. Subsequently, our recommended approach is the new ‘tri-PRI’ index robust to constitutive pigment pool sizes across the canopy profile. tri-PRI is a Triangular Vegetation Index (TVI) (tri-PRI = 0.5[(520 - 490)(R545nm - R490nm) - (545 – 490)(R520nm - R490nm)]) using three reflectance bands around 490 nm, 520 nm and 545 nm, and has a physiological photosynthetic basis. We found that tri-PRI significantly outperformed PRI and other two band combinations for quantifying the xanthophyll EPoxidation State ‘EPS’ (tri-PRI R2 = 0.75 versus PRI R2 = 0.23), as well as the ΦNPQ and ΦPSII active chlorophyll fluorescence quenching yields. The new band placement enhanced the dynamic EPS absorption peak, while the third band provided an additional normalisation to minimise the confounding effects of pigments with overlapping spectral features. tri-PRI also performed comparably to parametric and non-parametric hyperspectral techniques and formulations using continuous spectral regions, highlighting the utility of targeted multispectral indices over hyperspectral approaches. This leaf-level study represents a foundational step toward indirectly measuring dynamic photosynthetic activity across the canopy profile in a tall mature Eucalypt forest to inform upscaling efforts from above-canopy remote sensing platforms. The application of tri-PRI and other top-performing multi-band TVI formulations for predicting EPS presented here should be explored across different canopy types, temporal-, and spatial scales.
Why it matches plant phenotyping methods葉レベルの反射スペクトルから光合成生理状態を推定する新規指標tri-PRIを開発し、既存指標や他のスペクトル手法と定量比較・検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we quantitatively evaluate the effectiveness of leaf-level parametric and non-parametric spectral formulations, band locations, and number of bands to track the xanthophyll pigment cycle in a tall mature Eucalypt forest.
Plantation forestry, in which trees are grown as a crop, must maintain wood production over repeated harvest cycles (rotations) to meet global wood demands on a limited land area. We analyze 33 yr of Landsat observations across the world's most productive forestry system, Eucalyptus plantations in southeastern Brazil, to assess long-term regional trends in wood production. We apply a simple algorithm to time series of the vegetation index NIRv in thousands of Eucalyptus stands to detect the starts and ends of rotations. We then estimate wood production in each identified stand and rotation, based on a statistical relationship between NIRv trajectories and inventory data from three plantation companies. We also compare Eucalyptus NIRv with that of surrounding native vegetation to assess the relative influence of management and environment on plantation productivity trends. Across more than 3,500 stands with three complete rotations between 1984 and 2016, modeled wood volume decreased significantly between the first and second rotation, but recovered at least partially in the third; mean wood volumes for the three rotations were 262, 228, and 247 m 3 /ha. This nonlinear trend reflects intensifying plantation management, as rotation length decreased by an average of 15% (decreasing wood volume per rotation) and NIRv proxies of tree growth rates increased (increasing volume) between the first and third rotation. However, NIRv also increased significantly over time in unmanaged vegetation around the plantations, suggesting that environmental trends affecting all vegetation also contribute to sustaining wood production. Management inputs will likely continue to be important for maintaining wood production in future harvests.
Why it matches plant phenotyping methodsLandsat時系列とNIRvから林分・輪伐ごとの開始終了を検出し、在庫データとの統計関係で木材生産量を推定する再利用可能な植物形質推定ワークフローが、研究の主要手法として記述されている。
abstractWe apply a simple algorithm to time series of the vegetation index NIRv in thousands of Eucalyptus stands to detect the starts and ends of rotations.
Recent advances and commercialisation of unmanned aerial vehicle/red blue green (RGB) camera systems and digital photogrammetric techniques now provide a cheap and flexible alternative to higher-cost airborne platforms for routine monitoring of canopy health in timber plantations. Structure-from-Motion photogrammetry produces very dense three-dimensional (3D) point clouds which can be used to derive metrics for inventory estimation. Unmanned aerial vehicle RGB photography also captures data that can relate to tree health. In contrast to the more common use of orthorectified RGB photography to extract this spectral information, we used the software package Agisoft Photoscan to assign a simple Vegetation Index value directly to each point in the 3D point cloud. Using data acquired by a DJI Phantom 4 Pro, we present a simple processing and photogrammetric workflow solution for detecting dead and dying trees in a young Eucalyptus pellita plantation located in the provenance of Riau, Sumatra. Trees affected by the bacterial wilt Ralstonia sp. present symptoms of necrotic foliage on individual branches or the whole crown. Assigning the Visible Atmospheric Resistant Index Vegetation Index colour-coded values to individual points in the 3D point cloud significantly enhanced visualisation of necrotic foliage on individual trees in both the point cloud and the associated orthophoto compared to the RGB equivalent images. This approach could easily be operationally deployed for the rapid detection and mapping of unhealthy trees with symptoms of necrotic foliage.
Why it matches plant phenotyping methodsUAV RGB画像と3D点群に植生指数を付与し、個体ごとの壊死葉・樹冠症状を検出する処理・写真測量ワークフローが中心であり、植物病害状態の表現型計測に該当する。
abstractwe present a simple processing and photogrammetric workflow solution for detecting dead and dying trees in a young Eucalyptus pellita plantation
Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that the mistletoe infection rate was relatively high, with 69% of the Eucalyptus fibrosa and 75% of the E. moluccana trees being infected. Next, to study the potential consequences of the infection for the trees, canopy temperatures of mistletoe plants and of infected and uninfected trees were analyzed using thermal imagery acquired during 10 flights with an unmanned aerial vehicle (UAV) in two consecutive summer seasons. Throughout all flight campaigns, mistletoe canopy temperature was 0.3–2 K lower than the temperature of the eucalypt canopy it was growing in, suggesting higher transpiration rates. Differences in canopy temperature between infected eucalypt foliage and mistletoe were particularly large when incoming radiation peaked. In these conditions, eucalypt foliage from infected trees also had significantly higher canopy temperatures (and likely lower transpiration rates) compared to that of uninfected trees of the same species. The study demonstrates the potential of using UAV-based infrared thermography for studying plant-water relations of mistletoe and its hosts.
Why it matches plant phenotyping methodsUAV赤外線サーモグラフィーによる植物キャノピー温度の取得・比較が研究の中心で、植物の蒸散・水分関係という生理形質を評価する方法として実証されている。
titleCan UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees?
EucalyptusMultispectral / hyperspectralFlowerObject detectionGrowth / development / phenology
Abstract. Recent studies have shown that in the spectral space there is often a better spectral separation between leaves and flowers and even between flowers of different species than between leaves of different species. In this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (Corymbia calophylla) in Western Australia, the state’s largest annual honey crop. Spectroradiometer measurements of flowers, leaves and groundcover from Red Gum forests were subjected to ANOVA analysis, which showed that flowers are spectrally different to their environment for 92 % of the wavelengths between 350 nm and 1800 nm. A more detailed assessment, using the JM Distance calculation, showed that the spectra can be reliably separated using 10 % of the wavelengths, with peak separation between 518 nm and 557 nm. To assess the ability of satellite-borne sensors to detect the presence of flowers, the spectroradiometer data were convolved with satellite instruments’ response curves to create synthetic remotely sensed datasets on which JM Distance analysis was performed. MODIS blue bands achieved a median JM Distance of greater than 1.9 and therefore should be able to detect the presence of flowers from the environment. Further assessment showed that the shortest wavelength bands for MODIS, VIIRS and Sentinel 3 all occur where the flower spectra have lower reflectance than their natural background. A sensitivity analysis of percentage flower cover for a pixel showed that the highest sensitivity was obtained by dividing the band closest to 520 nm by the shortest wavelength band for data from these three sources. The MODIS band 10/band 8 metric was tested for its ability to detect flowers in real-world data using 15 years of qualitative honey harvest data from one apiary site as a proxy for flower density. This test was successful as, while there was some overlap between good, moderate and poor years, the poor years could be separated from the other years with nearly 80 % accuracy.
Why it matches plant phenotyping methods衛星リモートセンシングによる樹木の開花状態検出を中心に、分光分離、センサー帯域評価、実データでの検証を行っているため、植物フェノタイピング手法として適格です。
abstractIn this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (Corymbia calophylla) in Western Australia
Aim of study: In this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands.Area of study: Two commercial forest stands were selected, with two plots each. The forest species studied were Eucalyptus spp. and Pinus taeda L. and the trees had an age of 1.5 years.Material and methods: The individual trees were detected based on watershed segmentation and local maxima, using the spectral values stored in the point cloud. After the tree detection, the heights were calculated using two approaches, in the first one using the Digital Surface Model (DSM) and a Digital Terrain Model, and in the second using only the DSM. We used the UAV-derived heights to estimate an uniformity index.Main results: The trees were detected with a maximum 6% of error. However, the height was underestimated in all cases, in an average of 1 and 0.7 m for Pinus and Eucalyptus stands. We proposed to use the models built herein to estimate tree height, but the regression models did not explain the variably within the data satisfactorily. Therefore, the uniformity index calculated using the direct UAV-height values presented results close to the field inventory, reaching better results when using the second height approach (error ranging 2.8-7.8%).Research highlights: The uniformity index using the UAV-derived height from the proposed methods was close to the values obtained in field. We noted the potential for using UAV imagery in forest monitoring.
Why it matches plant phenotyping methodsUAV画像からSfM点群を生成し、個体検出・樹高推定・均一性指数算出を行い、現地測定と比較検証しているため、植物形質取得手法が研究の中心です。
abstractIn this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands.
Highly accurate, rapid forest inventory techniques are needed to enable forest managers to address the increasing demand for sustainable forestry. In the last two decades, Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning have become internationally established as forest mapping and monitoring methods. However, recent advances in sensors and in image processing – particularly Structure from Motion (SfM) technology – have also enabled the extraction of dense point clouds from images obtained by Digital Aerial Photography (DAP). DAP is cheaper than ALS, especially when the systems are mounted on small unmanned aerial vehicles (UAVs), and the density of the point cloud can easily reach the levels yielded by ALS devices. The main objective of this study was to evaluate and compare the usefulness of ALS-derived and UAV(SfM)-derived high-density point clouds for detecting and measuring individual tree height in Eucalyptus spp. plantations established on complex terrain. A total of 325 reference trees were measured and located in 6 square plots (400 m2). The individual tree crown (ITC) delineation algorithm detected 311 from the ALS-derived data and 259 trees from the UAV(SfM)-derived data, representing accuracy levels of, respectively, 96% and 80%. The results suggest that at plot level, UAV(SfM)-generated point clouds are as good as ALS-derived point clouds for estimating individual tree height. Furthermore, analysis of the differences in digital elevation models at landscape level showed that the elevations of the UAV(SfM)-derived terrain surfaces were slightly higher than the ALS-derived surfaces (mean difference, 1.14 m and standard deviation, 1.93 m). Finally, we discuss how non-optimal UAV-image-acquisition conditions and slope terrain affect the ITC delineation process.
Why it matches plant phenotyping methodsALSおよびUAV-SfM点群を用いた個体樹の検出・樹高測定手法を比較評価しており、植物形質の取得精度が研究の中心である。
abstractThe main objective of this study was to evaluate and compare the usefulness of ALS-derived and UAV(SfM)-derived high-density point clouds for detecting and measuring individual tree height in Eucalyptus spp. plantations established on complex terrain.
There is high uncertainty in the contribution of land-use change to anthropogenic climate change, especially pertaining to below-ground carbon loss resulting from conversion of primary-to-secondary forest. Soil organic carbon (SOC) and coarse roots are concentrated close to tree trunks, a region usually unmeasured during soil carbon sampling. Soil carbon estimates and their variation with land-use change have not been correspondingly adjusted. Our aim was to deduce allometric equations that will allow improvement of SOC estimates and tree trunk carbon estimates, for primary forest stands that include large trees in rugged terrain. Terrestrial digital photography, photogrammetry and GIS software were used to produce 3D models of the buttresses, roots and humus mounds of large trees in primary forests dominated by Eucalyptus regnans in Tasmania. Models of 29, in situ eucalypts were made and analysed. 3D models of example eucalypt roots, logging debris, rainforest tree species, fallen trees, branches, root and trunk slices, and soil profiles were also derived. Measurements in 2D, from earlier work, of three buttress 'logs' were added to the data set. The 3D models had high spatial resolution. The modelling allowed checking and correction of field measurements. Tree anatomical detail was formulated, such as buttress shape, humus volume, root volume in the under-sampled zone and trunk hollow area. The allometric relationships developed link diameter at breast height and ground slope, to SOC and tree trunk carbon, the latter including a correction for senescence. These formulae can be applied to stand-level carbon accounting. The formulae allow the typically measured, inter-tree SOC to be corrected for not sampling near large trees. The 3D models developed are irreplaceable, being for increasingly rare, large trees, and they could be useful to other scientific endeavours.
Why it matches plant phenotyping methods樹木の根・幹・バットレス等の形態と体積を、写真測量・GISによる3Dモデルから抽出し、炭素量推定を改善する手法が研究の中心である。
abstractTerrestrial digital photography, photogrammetry and GIS software were used to produce 3D models of the buttresses, roots and humus mounds of large trees
The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05Ω on average near zenith. Clumping error increased linearly with zenith angle to >0.3Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1m of unrepresentative large tree stems, so these situations should be avoided in practice if possible.
Why it matches plant phenotyping methods仮想森林と全天空写真を用いて、林冠クラ ンピング係数およびLAI/PAI推定手法を比較・検証しており、植物構造形質の取得精度評価が研究の中心である。
abstractHere, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy
Eucalyptus oil is produced from a number of “oil mallee” species with high concentration of foliar essential oils, high proportion of 1,8-cineole and the ability to re-sprout with multiple stems from lignotubers after coppicing. Eucalyptus polybractea is one of the most planted mallees in Australia, but few efforts have been made to improve crop yields through selection or breeding programs, with the exception of seed collection from trees with high oil and 1,8-cineole concentration. The yield of essential oil from commercial eucalypt plantations is subject to several quantitative traits that interact, both positively and negatively. Using non-destructive methods we assessed traits relating to oil yield (quantitative and qualitative variation of foliar essential oils and biomass-related parameters) for their variability, heritability as well as phenotypic and genetic interactions in an open-pollinated progeny trial with 40 families and 480 individuals of E. polybractea. From this we built models to predict family yield performance and compared our predictions to commercial scale harvests of the same trial.Our models show that relying on oil concentration and 1,8-cineole proportion alone is not ideal for selection of top performing families. Rather a mixture of biomass related traits, foliar oil concentration, 1,8-cineole proportion and leaf architecture contribute to the top performing families in varying ways.
Why it matches plant phenotyping methods非破壊測定と予測モデルにより、葉の構造・バイオマス関連形質・油収量を推定し、商業収穫と比較検証しており、植物形質取得法が中心である。
titleAssessment of a non-destructive method to predict oil yield in Eucalyptus polybractea (blue mallee)
Background LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m -2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. Results The results show that LiDAR pulse density of 5 pulses m -2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m -2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. Conclusion LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m -2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.
Why it matches plant phenotyping methods航空LiDARと機械学習を用いたユーカリ林分の地上部炭素量という植物群落形質の推定について、パルス密度とグリッドサイズの影響を精度検証しており、フェノタイピング手法が中心である。
abstractThe aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data.
This research project set out to provide data workflow and analytical solutions required for the operational deployment of high spatial resolution 3D data acquired by light aircraft and Unmanned Airborne Vehicles (UAV) suitable for integration into the resource information systems managed by Australian plantation and native forest growers. A key finding of this multi-faceted research project was the robust performance of applications using point cloud data acquired from aerial photography (AP). A detailed evaluation of point clouds obtained from several AP platforms and coincident LiDAR data acquired over both P. radiata and eucalypt plantations revealed both the strengths and weaknesses of AP data processed through a modern photogrammetric solution. The studies reported here (Section 2) have helped to identify operational specifications for acquiring AP imagery. These findings are supported by a detailed description of data acquisition and processing workflows in Planning Guidelines that accompany this Final Report. Digital photography acquired from a manned aircraft or UAV can be processed using commercial software to produce a high resolution 3D canopy data. A photogrammetric approach depends on availability of a sufficiently accurate Digital Terrain Model, which is usually provided from prior LiDAR acquisition, although opportunistic capture of a DTM using AP prior to or soon after planting is an alternative to LiDAR. A key advantage of using AP to acquire Canopy Height Models (CHMs) is that AP data are likely to be cheaper to acquire than airborne LiDAR and so provide a cost-effective solution for inventory updates.
Why it matches plant phenotyping methods航空写真・UAV・LiDARによる3D点群と樹冠高モデル（CHM）の取得・処理ワークフローを評価し、植林地の樹冠高という植物形質を測定する方法の性能と運用仕様を検討しているため、方法検証・プラットフォーム研究として含める。
abstractA detailed evaluation of point clouds obtained from several AP platforms and coincident LiDAR data acquired over both P. radiata and eucalypt plantations revealed both the strengths and weaknesses of AP data processed through a modern photogrammetric solution.
Estimating the proportion of woody-to-total plant material ‘α’ is an essential step to convert Plant Area Index ‘PAI’ estimates into Leaf Area Index ‘LAI’. α has also been shown to have a significant impact on the passive optical remote sensing signal for retrieval of biophysical parameters in forests, woodlands, and savannas. However, benchmarked indirect α retrieval methods are lacking and thus it is common for this pivotal correction to be ignored. In this paper we validate an α retrieval method using a 3D radiative transfer simulation framework, enabling the retrieval method to be benchmarked against a known and precise model truth. The 3D framework consists of a representative and highly detailed 3D explicit Eucalypt forest reconstructed from field measurements. The 3D structure is coupled with a 3D scattering model to enable simulation of remote sensing instruments. The retrieval method utilises classified hemispherical photography ‘HP’, but is applicable to all ground-based optical instruments that can separate leaf and woody elements. The method is applicable to evergreen forests and thus independent of the estimation of PAI or LAI. The unknown degree of mutual shading or occlusion of leaf and woody elements was traditionally a key impediment to the operational use of this method and was therefore closely examined. The indirect α method utilising classified HP imagery agreed on average to within 0.01 α of the reference (αref=0.37). In addition, the method demonstrated robustness to a range of LAI, stem density, and stem distribution values, matching to within ±0.05 α of the reference. Angular dependence on indirect α retrieval was also found; where the entire HP image (180° FOV) was needed to produce the most accurate estimate. Conversely, the classified narrow view zenith angle range around 55−60° zenith also provided an α estimate matching the reference. At this narrow zenith angle the method is insensitive to leaf angle distribution. As such, careful consideration of zenith angle range utilised from the instrument is recommended. The results demonstrate the method’s applicability for accurate indirect estimation of α in single-storey forest types. The simple and efficient method can be used to convert estimates of PAI into LAI from a variety of optical ground-based instruments. Quantitative α estimates can and should be used to aid interpretation of the remote sensing signal from satellite imagery, which has been shown to be sensitive to the proportion and spatial distribution of woody canopy materials.
Why it matches plant phenotyping methods森林キャノピーの葉面積指数推定に用いる木質部割合αの取得法を、3Dシミュレーションでベンチマークおよび検証しており、植物形質推定手法が中心である。
abstractIn this paper we validate an α retrieval method using a 3D radiative transfer simulation framework, enabling the retrieval method to be benchmarked against a known and precise model truth.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Near-infrared reflectance spectroscopy (NIRS) is frequently used for the assessment of key nutrients of forage or crops but remains underused in ecological and physiological studies, especially to quantify non-structural carbohydrates. The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.
Why it matches plant phenotyping methods葉の非構造性炭水化物をNIRSで推定する校正モデルを開発し、独立試料で検証しており、植物生理形質の取得法が中心である。
abstractThe aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus.
Aim of study: The study aimed to characterise variation in structural attributes of vegetation in relation to variations in topographic position using LIDAR data over landscapes.Area of study: The study was conducted in open canopy eucalypt-dominated forest (Richmond Range National Park-RRNP) and closed canopy subtropical rainforest (Border Ranges National Park-BRNP) in north-eastern New South Wales, Australia.Material and Methods: one metre resolution digital canopy height model (CHM) was extracted from the LIDAR data and used to estimate maximum overstorey height and crown area. LIDAR fractional cover representing the photosynthetic and non-photosynthetic component of canopy was calculated using LIDAR points aggregated into 50 m spatial bins. Potential solar insolation, Topographic Wetness Index (TWI), slope and the elevation were processed using LIDAR derived digital elevation models.Main results: No relationship was found between maximum overstorey height and insolation gradient in the BRNP. Maximum overstorey height decreased with increasing insolation in the RRNP (R2 0.45). Maximum overstorey height increased with increasing TWI in the RRNP. Average crown area decreased with increasing insolation in both study areas. LIDAR fractional cover decreased with increasing insolation (R2 0.54), and increased with increasing TWI (R2 0.57) in the RRNP.Research highlights: The characterization of structural parameters of vegetation in relation to the variation of the topography was possible in eucalyptus dominated open canopy forest. No reportable difference in variation of structural elements of vegetation was detected with topographic variation of subtropical rainforest.
Why it matches plant phenotyping methodsLIDARを用いて森林の樹冠高、樹冠面積、樹冠被覆率という植物構造形質を抽出し、地形との関係を評価する測定ワークフローが研究の中心である。
abstractLIDAR fractional cover representing the photosynthetic and non-photosynthetic component of canopy was calculated using LIDAR points aggregated into 50 m spatial bins.
Abstract Accurate ground‐based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost‐effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above‐ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power‐law models explained 84–95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand‐based biomass from allometric models of varying levels of generalization (species‐specific, plant functional type) were validated using whole‐plot harvest data from 17 contrasting stands (range: 9–356 Mg ha −1 ). Losses in efficiency of prediction were stand ‐level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost‐effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species‐specific models is only warranted when gains in accuracy of stand‐based predictions are relatively high (e.g. high‐value monocultures).
Why it matches plant phenotyping methods植物の地上部バイオマスを推定するアロメトリーモデルの一般性と予測性能を大規模データおよび全区画収穫データで検証しており、形質推定手法の技術的検証が中心である。
abstractAllometric models provide cost‐effective methods for biomass prediction.
This study investigates the potential of unmanned aerial vehicles (UAVs) to measure and monitor structural properties of forests. Two remote sensing techniques, airborne laser scanning (ALS) and structure from motion (SfM) were tested to capture three-dimensional structural information from a small multi-rotor UAV platform. A case study is presented through the analysis of data collected from a 30 × 50 m plot in a dry sclerophyll eucalypt forest with a spatially varying canopy cover. The study provides an insight into the capabilities of both technologies for assessing absolute terrain height, the horizontal and vertical distribution of forest canopy elements, and information related to individual trees. Results indicate that both techniques are capable of providing information that can be used to describe the terrain surface and canopy properties in areas of relatively low canopy closure. However, the SfM photogrammetric technique underperformed ALS in capturing the terrain surface under increasingly denser canopy cover, resulting in point density of less than 1 ground point per m2 and mean difference from ALS terrain surface of 0.12 m. This shortcoming caused errors that were propagated into the estimation of canopy properties, including the individual tree height (root mean square error of 0.92 m for ALS and 1.30 m for SfM). Differences were also seen in the estimates of canopy cover derived from the SfM (50%) and ALS (63%) pointclouds. Although ALS is capable of providing more accurate estimates of the vertical structure of forests across the larger range of canopy densities found in this study, SfM was still found to be an adequate low-cost alternative for surveying of forest stands.
Why it matches plant phenotyping methodsUAV搭載ALSとSfMによる森林キャノピー・個体樹高・被覆率の取得を比較検証しており、植物構造形質の計測手法が中心である。
abstractTwo remote sensing techniques, airborne laser scanning (ALS) and structure from motion (SfM) were tested to capture three-dimensional structural information from a small multi-rotor UAV platform.