Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery
Abstract Leaf water content (LWC) encapsulates critical aspects of tree physiology and is considered a proxy for assessing tree drought stress and the risk of forest decline; however, its measurement relies on destructive sampling and is thus less efficient. Advancements in hyperspectral imaging tec...
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BMC
2024-12-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-024-01312-1 |
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| author | Zhao-Kui Li Hong-Li Li Xue-Wei Gong Heng-Fang Wang Guang-You Hao |
| author_facet | Zhao-Kui Li Hong-Li Li Xue-Wei Gong Heng-Fang Wang Guang-You Hao |
| author_sort | Zhao-Kui Li |
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| description | Abstract Leaf water content (LWC) encapsulates critical aspects of tree physiology and is considered a proxy for assessing tree drought stress and the risk of forest decline; however, its measurement relies on destructive sampling and is thus less efficient. Advancements in hyperspectral imaging technology present new prospects for noninvasively evaluating LWC and mapping drought severity across forested regions. In this study, leaf samples were obtained from Populus alba var. pyramidalis, a species widely employed for constructing farmland shelterbelts in water-limited regions of northern China but notably susceptible to drought. These samples were dehydrated to varying degrees to generate concurrent LWC measurements and hyperspectral images, enabling the development of narrow-band and multivariate spectral prediction models for LWC estimation. Two visible-spectrum narrow-band indices identified, the single-band index (R 627) and the band subtraction index (R 437 − R 444), demonstrated a strong correlation with LWC. Despite certain influences of variable preprocessing and selection on multivariate model performance, most models exhibited robust predictive accuracy for LWC. The FDRL-UVE-PLSR combination emerged as the optimal multivariate model, with R 2 values reaching 0.9925 and 0.9853 and RMSE values below 0.0124 and 0.0264 for the calibration and validation datasets, respectively. Using this optimal model, along with localized spectral smoothing, moisture distribution across leaf surfaces was visualized, revealing lower water retention at the leaf margins compared to central regions. These methodologies provide critical insights into subtle water-associated physiological processes at the leaf scale and facilitate high-frequency, large-scale assessments and monitoring of drought stress levels and the risk of drought-induced tree mortality and forest degradation in drylands. |
| format | Article |
| id | doaj-art-d540967ce73e440dbe821f2fd6ecb736 |
| institution | OA Journals |
| issn | 1746-4811 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
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| series | Plant Methods |
| spelling | doaj-art-d540967ce73e440dbe821f2fd6ecb7362025-08-20T01:57:15ZengBMCPlant Methods1746-48112024-12-0120111710.1186/s13007-024-01312-1Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imageryZhao-Kui Li0Hong-Li Li1Xue-Wei Gong2Heng-Fang Wang3Guang-You Hao4School of Computer Science, Shenyang Aerospace UniversitySchool of Computer Science, Shenyang Aerospace UniversityCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of SciencesKey Laboratory of Oasis Ecology of Education Ministry, College of Ecology and Environment, Xinjiang UniversityCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of SciencesAbstract Leaf water content (LWC) encapsulates critical aspects of tree physiology and is considered a proxy for assessing tree drought stress and the risk of forest decline; however, its measurement relies on destructive sampling and is thus less efficient. Advancements in hyperspectral imaging technology present new prospects for noninvasively evaluating LWC and mapping drought severity across forested regions. In this study, leaf samples were obtained from Populus alba var. pyramidalis, a species widely employed for constructing farmland shelterbelts in water-limited regions of northern China but notably susceptible to drought. These samples were dehydrated to varying degrees to generate concurrent LWC measurements and hyperspectral images, enabling the development of narrow-band and multivariate spectral prediction models for LWC estimation. Two visible-spectrum narrow-band indices identified, the single-band index (R 627) and the band subtraction index (R 437 − R 444), demonstrated a strong correlation with LWC. Despite certain influences of variable preprocessing and selection on multivariate model performance, most models exhibited robust predictive accuracy for LWC. The FDRL-UVE-PLSR combination emerged as the optimal multivariate model, with R 2 values reaching 0.9925 and 0.9853 and RMSE values below 0.0124 and 0.0264 for the calibration and validation datasets, respectively. Using this optimal model, along with localized spectral smoothing, moisture distribution across leaf surfaces was visualized, revealing lower water retention at the leaf margins compared to central regions. These methodologies provide critical insights into subtle water-associated physiological processes at the leaf scale and facilitate high-frequency, large-scale assessments and monitoring of drought stress levels and the risk of drought-induced tree mortality and forest degradation in drylands.https://doi.org/10.1186/s13007-024-01312-1Drought stressForest healthHyperspectral reflectanceLeaf water statusMachine learningPrediction model |
| spellingShingle | Zhao-Kui Li Hong-Li Li Xue-Wei Gong Heng-Fang Wang Guang-You Hao Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery Plant Methods Drought stress Forest health Hyperspectral reflectance Leaf water status Machine learning Prediction model |
| title | Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery |
| title_full | Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery |
| title_fullStr | Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery |
| title_full_unstemmed | Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery |
| title_short | Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery |
| title_sort | prediction and mapping of leaf water content in populus alba var pyramidalis using hyperspectral imagery |
| topic | Drought stress Forest health Hyperspectral reflectance Leaf water status Machine learning Prediction model |
| url | https://doi.org/10.1186/s13007-024-01312-1 |
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