Heritable variation in tree growth and needle vegetation indices of slash pine (Pinus elliottii) using unmanned aerial vehicles (UAVs)

Tao, Xiaofeng; Li, Yanjie; Yan, Weiqi; Wang, Minjuan; Tan, Zifeng; Jiang, Jingmin; Luan, Qifu

doi:10.1016/j.indcrop.2021.114073

Cited by 18 publications

(11 citation statements)

References 71 publications

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“…UAV-based RGB imagery has been successfully applied to numerous conifer forests for tree growth measurements [ 30 , 31 ]. Previously, study has been shown that UAV-multispectral platforms could serve as an rapid method for breeding selection of vegetation indices in slash pine trees [ 32 ]. However, prior to this project, UAV-based RGB imagery has not been employed in slash pine plantations for breeding purposes.…”

Section: Introductionmentioning

confidence: 99%

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging

et al. 2022

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Traditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (Pinus elliottii) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle (UAV) integrated with structure from motion (UAV-SfM) data to estimate the tree height, crown area (CA), AGB, and BGB of slash pine for in slash pine breeding plantations sites. The CA of each tree was segmented by using marker-controlled watershed segmentation with a treetop and a set of minimum three meters heights. Moreover, the genetic variation of these traits has been analyzed and employed to estimate heritability (h2). The results showed a promising correlation between UAV and ground truth data with a range of R2 from 0.58 to 0.85 at 70 m flying heights and a moderate estimate of h2 for all traits ranges from 0.13 to 0.47, where site influenced the h2 value of slash pine trees, where h2 in site 1 ranged from 0.13~0.25 lower than that in site 2 (range: 0.38~0.47). Similar genetic gains were obtained with both UAV and ground truth data; thus, breeding selection is still possible. The method described in this paper provides faster, more high-throughput, and more cost-effective UAV-SfM surveys to monitor a larger area of breeding plantations than traditional ground surveys while maintaining data accuracy.

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Section: Introductionmentioning

confidence: 99%

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging

et al. 2022

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“…They used three classifiers (DT, SVM, and K-nearest neighbor), and finally the SVM-based classifier provided the best performance. Not only that, the rapid detection and classification of corn ( Zea mays L.) seeds by the SVM classification model yielded a high classification accuracy of 96.46% ( Tao et al, 2021 ). The results of these studies are consistent with the results of this study, where the SVM model shows better classification performance.…”

Section: Discussionmentioning

confidence: 99%

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Song

Niu

et al. 2022

Front. Plant Sci.

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Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.

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“…In crop science, canopy reflectance is commonly used to estimate aboveground biomass or yield ( Yang and Chen, 2004 ; Wang et al, 2017 ). In forest science, the tree height and aboveground biomass are usually modeled from canopy level lidar data or lidar ( Persson et al, 2002 ), hyperspectral data fusion ( Sankey et al, 2017 ), or UAV-borne image data with a very high spatial resolution ( Tao et al, 2021 ). In contrast, we tested the single wavelength shoot-level reflectance correlation to all three growth traits such as DBH, tree height, crown height, and length.…”

Section: Discussionmentioning

confidence: 99%

Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes

et al. 2022

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Norway spruce has a wide natural distribution range, harboring substantial physiological and genetic variation. There are three altitudinal ecotypes described in this species. Each ecotype has been shaped by natural selection and retains morphological and physiological characteristics. Foliar spectral reflectance is readily used in evaluating the physiological status of crops and forest ecosystems. However, underlying genetics of foliar spectral reflectance and pigment content in forest trees has rarely been investigated. We assessed the reflectance in a clonal bank comprising three ecotypes in two dates covering different vegetation season conditions. Significant seasonal differences in spectral reflectance among Norway spruce ecotypes were manifested in a wide-ranging reflectance spectrum. We estimated significant heritable variation and uncovered phenotypic and genetic correlations among growth and physiological traits through bivariate linear models utilizing spatial corrections. We confirmed the relative importance of the red edge within the context of the study site’s ecotypic variation. When interpreting these findings, growth traits such as height, diameter, crown length, and crown height allowed us to estimate variable correlations across the reflectance spectrum, peaking in most cases in wavelengths connected to water content in plant tissues. Finally, significant differences among ecotypes in reflectance and other correlated traits were detected.

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Heritable variation in tree growth and needle vegetation indices of slash pine (Pinus elliottii) using unmanned aerial vehicles (UAVs)

Cited by 18 publications

References 71 publications

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes

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