Measurement methods and variability assessment of the Norway spruce total leaf area: implications for remote sensing

Homolová, Lucie; Lukeš, Petr; Malenovský, Zbyněk; Lhotáková, Zuzana; Kaplan, Věroslav; Hanuš, Jan

doi:10.1007/s00468-012-0774-8

Cited by 23 publications

(25 citation statements)

References 40 publications

(1 reference statement)

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“…Interspecific variability in needle chlorophyll content is caused by needle aging: chlorophyll content of the current-year needles increases during the growing season compared to older needles, which have stable chlorophyll content (e.g., [60]). Older and shade-adapted needles typically exhibit higher chlorophyll content as demonstrated for Sitka spruce [17] or Norway spruce (Table 3 and [61]). Table 2.…”

Section: Drivers Of Spectral Variability In Needle Spectramentioning

confidence: 73%

“…Chl SLAh-specific leaf area calculated on needle hemisurface area basis; LAT CF-projected to total leaf area correction factor (see [61] for more details); Cab per dry matter-total needle chlorophyll a+b content expressed on needle dry matter basis; Car per dry matter-total needle carotenoid content expressed on needle dry matter basis; Cab per needle area-total needle chlorophyll a+b content expressed on hemisurface leaf area basis; Car per needle area-total needle carotenoid content expressed on hemisurface leaf area basis; Cm per needle area-needle dry matter content expressed on hemisurface leaf area basis; Cw per needle area-needle water content expressed on hemisurface leaf area basis.…”

Section: Species (N)mentioning

confidence: 99%

“…Most coniferous species have non-flat needles, and thus, the ratio between the total-to-projected leaf area varies between species, as well as within individual tree crowns, as adaptation to different incident light levels [46,61]. The shaded needles are flatter, which improves their light harvesting efficiency [46].…”

Section: Species (N)mentioning

confidence: 99%

“…The shaded needles are flatter, which improves their light harvesting efficiency [46]. Thus, different expressions of leaf chlorophyll (per mass or area basis) might show different patterns and gradients with respect to needle age and illumination conditions, and must be interpreted and cross-compared with care [46,61]. As shown in the example of the within-species variability of Norway spruce biochemical properties (Table 3), pigments per mass weight show stronger differences between needles of different age and light exposures than the equivalent expressed per hemisurface leaf area.…”

Section: Species (N)mentioning

confidence: 99%

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Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements

Rautiainen

Lukeš²,

Homolová³

et al. 2018

Remote Sensing

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108

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Abstract:Coniferous species are present in almost all major vegetation biomes on Earth, though they are the most abundant in the northern hemisphere, where they form the northern tree and forest lines close to the Arctic Circle. Monitoring coniferous forests with satellite and airborne remote sensing is active, due to the forests' great ecological and economic importance. We review the current understanding of spectral behavior of different components forming coniferous forests. We look at the spatial, directional, and seasonal variations in needle, shoot, woody element, and understory spectra in coniferous forests, based on measurements. Through selected case studies, we also demonstrate how coniferous canopy spectra vary at different spatial scales, and in different viewing angles and seasons. Finally, we provide a synthesis of gaps in the current knowledge on spectra of elements forming coniferous forests that could also serve as a recommendation for planning scientific efforts in the future.

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Section: Drivers Of Spectral Variability In Needle Spectramentioning

confidence: 73%

Section: Species (N)mentioning

confidence: 99%

Section: Species (N)mentioning

confidence: 99%

Section: Species (N)mentioning

confidence: 99%

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Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements

Rautiainen

Lukeš²,

Homolová³

et al. 2018

Remote Sensing

Self Cite

108

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show abstract

“…Errors are introduced -particularly when calculating the area of conifer needle samples -by the shadow effect while scanning samples, while classifying the scanned images to binary format, while rounding off pixels, by the correction factor used and by other procedures. Studies showed that the shape of the Norway spruce needles vary for different age classes and need different conversion factors (Homolova et al 2013). But we simply used a universal conversion factor from literature which could be source of error.…”

Section: Discussionmentioning

confidence: 99%

Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

Ali

Darvishzadeh

Skidmore

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Quantification of vegetation properties plays an indispensable role in assessments of ecosystem function with leaf dry mater content (LDMC) and specific leaf area (SLA) being two important vegetation properties. Methods for fast, reliable and accurate measurement of LDMC and SLA are still lacking. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate these two leaf parameters. Inversion of PROSPECT traditionally aims at quantifying its direct input parameters rather than identifying the parameters which can be derived indirectly from the input parameters. The technique has been tested here to indirectly model these parameters for the first time. Biophysical parameters such as leaf area, as well as fresh and dry weights of 137 leaf samples were measured during a field campaign in July 2013 in the mixed mountain forests of the Bavarian Forest National Park, Germany. Reflectance and transmittance of the leaf samples were measured using an ASD field spec III equipped with an integrating sphere. The PROSPECT model was inverted using a look-up table (LUT) approach for the NIR/SWIR region of the spectrum. The retrieved parameters were evaluated using their calculated R 2 and normalized root mean square error (nRMSE) values with the field measurements. Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R 2 values (0.83 for LDMC and 0.89 for SLA) were discovered. The results indicate that the leaf traits studied can be quantified as accurately as the direct input parameters of PROSPECT. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy and in the landscape by using hyperspectral remote sensing data.

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Norway spruce needle size and cross section shape variability induced by irradiance on a macro- and microscale and CO2 concentration

Kubínová

Janáček

Lhotáková

et al. 2017

Trees

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Measurement methods and variability assessment of the Norway spruce total leaf area: implications for remote sensing

Cited by 23 publications

References 40 publications

Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements

Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements

Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

Norway spruce needle size and cross section shape variability induced by irradiance on a macro- and microscale and CO2 concentration

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