Spectral signatures discrimination of Norway spruce trees under experimentally induced drought and acute thermal stress using hyperspectral imaging

Climate change intensifies drought and thermal stress in forests, weakening tree vitality and increasing susceptibility to Ips typographus (L.) infestations. This study evaluates the potential of hyperspectral remote sensing to detect physiological stress in Norway spruce ( Picea abies (L.) Karst.) induced by chronic drought and acute thermal stress. An in situ manipulation experiment was conducted within a mature spruce stand, establishing precipitation-exclusion roofs (chronic drought), stand edges (acute thermal stress), and control plots. High resolution hyperspectral imagery data was collected across visible, NIR, and SWIR wavelengths (380–2500 nm) during six flights (2022–2023) using CASI-1500 (0.5 m) and SASI-600 (1.25 m) hyperspectral cameras. Spectral discrimination revealed significant differences between stressed and control trees, particularly in the red (669.58 nm), NIR (854.96 nm), and SWIR (1557.5 nm, 2082.5 nm) wavebands, while the green region (498.56 nm) separated better in reflectance than in derivative analyses. These wavebands effectively identified physiological changes, including pigment depletion, reduced water content, and nitrogen fluctuations. Spectral separations were strongest in late summer and autumn, coinciding with intensified stress responses. Acute stress showed variable NIR reflectance trends, increasing in autumn but declining in August 2023. Chronic stress exhibited higher SWIR reflectance, indicating reduced needle water content. Statistical analyses, including the Kruskal-Wallis test and post hoc Dunn and Mann-Whitney U tests, confirmed significant spectral separability between stress types. These spectral markers provide a framework for monitoring tree stress under variable environmental conditions. By integrating remote sensing with climatic data, this study demonstrates how hyperspectral imaging enables early stress detection, supporting proactive forest management against climate-induced threats. • Spectral analysis distinguishes stress types and no-stress conditions in trees. • Hyperspectral bands detect drought and acute thermal stress on Norway spruce. • Seasonal stress responses are most distinct in late summer and autumn. • Certain spectral bands are crucial for detecting canopy stress. • Experimentally induced drought stress predisposes trees to bark beetle attacks.

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Publication Details

Journal
Forest Ecology and Management
Published
2025-04-01
DOI
https://doi.org/10.1016/j.foreco.2025.122692
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.43
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article

Spectral signatures discrimination of Norway spruce trees under experimentally induced drought and acute thermal stress using hyperspectral imaging

Jaroslav Rožnovský, Eija Honkavaara, Roope Näsi, Jaroslav Škvarenina et al.
2 citations
Forest Ecology and Management
Remote Sensing in Agriculture
3.43
article

Spectral signatures discrimination of Norway spruce trees under experimentally induced drought and acute thermal stress using hyperspectral imaging

Jaroslav Rožnovský, Eija Honkavaara, Roope Näsi, Jaroslav Škvarenina, Miroslav Blaženec, Roman Modlinger, Rastislav Jakuš, Matúš Pivovar
article en
2 citations

Abstract

Climate change intensifies drought and thermal stress in forests, weakening tree vitality and increasing susceptibility to Ips typographus (L.) infestations. This study evaluates the potential of hyperspectral remote sensing to detect physiological stress in Norway spruce ( Picea abies (L.) Karst.) induced by chronic drought and acute thermal stress. An in situ manipulation experiment was conducted within a mature spruce stand, establishing precipitation-exclusion roofs (chronic drought), stand edges (acute thermal stress), and control plots. High resolution hyperspectral imagery data was collected across visible, NIR, and SWIR wavelengths (380–2500 nm) during six flights (2022–2023) using CASI-1500 (0.5 m) and SASI-600 (1.25 m) hyperspectral cameras. Spectral discrimination revealed significant differences between stressed and control trees, particularly in the red (669.58 nm), NIR (854.96 nm), and SWIR (1557.5 nm, 2082.5 nm) wavebands, while the green region (498.56 nm) separated better in reflectance than in derivative analyses. These wavebands effectively identified physiological changes, including pigment depletion, reduced water content, and nitrogen fluctuations. Spectral separations were strongest in late summer and autumn, coinciding with intensified stress responses. Acute stress showed variable NIR reflectance trends, increasing in autumn but declining in August 2023. Chronic stress exhibited higher SWIR reflectance, indicating reduced needle water content. Statistical analyses, including the Kruskal-Wallis test and post hoc Dunn and Mann-Whitney U tests, confirmed significant spectral separability between stress types. These spectral markers provide a framework for monitoring tree stress under variable environmental conditions. By integrating remote sensing with climatic data, this study demonstrates how hyperspectral imaging enables early stress detection, supporting proactive forest management against climate-induced threats. • Spectral analysis distinguishes stress types and no-stress conditions in trees. • Hyperspectral bands detect drought and acute thermal stress on Norway spruce. • Seasonal stress responses are most distinct in late summer and autumn. • Certain spectral bands are crucial for detecting canopy stress. • Experimentally induced drought stress predisposes trees to bark beetle attacks.

Forest Ecology and ManagementVol. 586
Institute of Chemistry of the Slovak Academy of Sciences (SK), Czech Hydrometeorological Institute (CZ), Czech University of Life Sciences Prague (CZ), Finnish Geospatial Research Institute (FI), Institute of Forest Ecology of the Slovak Academy of Sciences (SK), Technická univerzita vo Zvolene (SK), Earth Science Institute of the Slovak Academy of Sciences (SK)
Openalex Percentile: Top 10%
Remote Sensing in Agriculture
3.43
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