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Zhang Y, Wang A, Li J, Wu J. Water content estimation of conifer needles using leaf-level hyperspectral data. FRONTIERS IN PLANT SCIENCE 2024; 15:1428212. [PMID: 39309177 PMCID: PMC11412879 DOI: 10.3389/fpls.2024.1428212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 08/20/2024] [Indexed: 09/25/2024]
Abstract
Water is a crucial component for plant growth and survival. Accurately estimating and simulating plant water content can help us promptly monitor the physiological status and stress response of vegetation. In this study, we constructed water loss curves for three types of conifers with morphologically different needles, then evaluated the applicability of 12 commonly used water indices, and finally explored leaf water content estimation from hyperspectral data for needles with various morphology. The results showed that the rate of water loss of Olgan larch is approximately 8 times higher than that of Chinese fir pine and 21 times that of Korean pine. The reflectance changes were most significant in the near infrared region (NIR, 780-1300 nm) and the short-wave infrared region (SWIR, 1300-2500 nm). The water sensitive bands for conifer needles were mainly concentrated in the SWIR region. The water indices were suitable for estimating the water content of a single type of conifer needles. The partial least squares regression (PLSR) model is effective for the water content estimation of all three morphologies of conifer needles, demonstrating that the hyperspectral PLSR model is a promising tool for estimating needles water content.
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Affiliation(s)
- Yuan Zhang
- CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
| | - Anzhi Wang
- CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
| | - Jiaxin Li
- CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jiabing Wu
- CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
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Stejskal J, Čepl J, Neuwirthová E, Akinyemi OO, Chuchlík J, Provazník D, Keinänen M, Campbell P, Albrechtová J, Lstibůrek M, Lhotáková Z. Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0111. [PMID: 38026471 PMCID: PMC10644830 DOI: 10.34133/plantphenomics.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023]
Abstract
Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.
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Affiliation(s)
- Jan Stejskal
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Jaroslav Čepl
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Eva Neuwirthová
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Olusegun Olaitan Akinyemi
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Jiří Chuchlík
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Daniel Provazník
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Markku Keinänen
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
- Center for Photonic Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Petya Campbell
- Department of Geography and Environmental Sciences,
University of Maryland Baltimore County, Baltimore, MD, USA
- Biospheric Sciences Laboratory,
NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Jana Albrechtová
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Milan Lstibůrek
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Zuzana Lhotáková
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
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Hejtmánek J, Stejskal J, Čepl J, Lhotáková Z, Korecký J, Krejzková A, Dvořák J, Gezan SA. Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes. FRONTIERS IN PLANT SCIENCE 2022; 13:721064. [PMID: 35712586 PMCID: PMC9197180 DOI: 10.3389/fpls.2022.721064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
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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Affiliation(s)
- Jakub Hejtmánek
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czechia
| | - Jan Stejskal
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czechia
| | - Jaroslav Čepl
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czechia
| | - Zuzana Lhotáková
- Department of Experimental Plant Biology, Faculty of Science, Charles University in Prague, Prague, Czechia
| | - Jiří Korecký
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czechia
| | - Anna Krejzková
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czechia
| | - Jakub Dvořák
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czechia
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Leaf Age Matters in Remote Sensing: Taking Ground Truth for Spectroscopic Studies in Hemiboreal Deciduous Trees with Continuous Leaf Formation. REMOTE SENSING 2021. [DOI: 10.3390/rs13071353] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
We examined the seasonal changes in biophysical, anatomical, and optical traits of young leaves, formed throughout the vegetative season due to sylleptic growth, and mature leaves formed by proleptic growth in spring. Leaf developmental categories contribute to the top-of-canopy reflectance and should be considered when taking ground truth for remote sensing studies (RS). Deciduous tree species, Betula pendula, Populus tremula, and Alnus incana, were sampled from May to October 2018 in an Estonian hemiboreal forest. Chlorophyll and carotenoid content were detected biochemically; leaf anatomical traits (leaf, palisade, and spongy mesophyll thickness) were measured on leaf cross-sections; leaf reflectance was measured by a spectroradiometer with an integrating sphere (350–2500 nm). Biophysical and anatomical leaf traits were related to 64 vegetation indices (VIs). Linear models based on VIs for all tested leaf traits were more robust if both juvenile and mature leaves were included. This study provides information on which VIs are interchangeable or independent. Pigment and leaf thickness sensitive indices formed PC1; water and structural trait related VIs formed an independent group associated with PC3. Type of growth and leaf age could affect the validation of biophysical and anatomical leaf trait retrieval from the optical signal. It is, therefore, necessary to sample both leaf developmental categories—young and mature—in RS, especially if sampling is only once within the vegetation season.
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Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV). REMOTE SENSING 2021. [DOI: 10.3390/rs13040705] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In this study, multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top, and height, as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge, and Near infrared (NIR) bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested: (i) to what extent can the linear relationship be established between selected vegetation indexes (Normalized Difference Vegetation Index (NDVI) and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents) and (ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have the potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level; however, the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the second year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data that were acquired by the UAV platform at a very high spatial resolution.
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